Mass Media Representation of Men and Women

The contemporary society is made up of different kinds of people shaped by the ideas represented in the media and the popular culture.Advertising We will write a custom essay sample on Mass Media Representation of Men and Women specifically for you for only $16.05 $11/page Learn More Generally, the ideal man is portrayed according to the views of the media and in most cases; many individuals do everything possible to resemble the people represented in the media. Being a black American in a white society has its difficulties as many stereotypes are associated with this particular race. All these stereotypes and differences are majorly influenced by the ideas represented in the media. This is further exacerbated by the position the media plays as an influential agent of socialization that has the capacity to influence even the other agents of socialization such as peers, the family and the family or school institutions. JHally in his documentary â€Å"Des ire, sex and power in music videos†, talks about women and men who are represented in an overly exaggerated manner as opposed to a real representation. He describes the women represented in music videos as a fantasy of the adolescents. The ideal woman is defined as one who is slim and light skinned in complexion, with blonde hair being overly exaggerated as a mark of beauty. The men portrayed in the music videos are also associated with certain features, which include the masculinity aspect.Advertising Looking for essay on communications media? Let's see if we can help you! Get your first paper with 15% OFF Learn More A real man is supposed to be masculine and thus, the girls who get to watch such kind of men may end up losing partners in their lives because they always look for the ideal man as represented in music videos and other visual media. Relationships are represented in a stereotypical manner. For instance, in Katz video â€Å"Tough Guise†, he an alyzes violence, media and crisis in masculinity. In this video, masculinity is analyzed in terms of relationships that exist between the male and other males and the males and females. In both videos, the aspects of racism, classism and heterosexism are represented. People relate with each other in terms of race and class. Thus, the blacks associate with fellow blacks and the whites too. Heterosexism is an aspect that is not strange according to the whites and according to the â€Å"Tough Guise† video male-male relationships are part of their relationships in their society. However, these two videos are against the stereotypes associated with human beings as they urge people to watch all videos with a critical eye. The directors of these videos say that, the media and popular culture can influence our thinking and lifestyle in a great way albeit all the views they represent are not true. The ideas and views in these videos are a mere representation of the adolescents’ fantasies. These representations relate to what I am today since, have always thought that the media represents the perfect man.Advertising We will write a custom essay sample on Mass Media Representation of Men and Women specifically for you for only $16.05 $11/page Learn More Popular culture has had an effect on me, as I have lived to believe that women who are viewed as pretty are petite and light skinned. On the other hand, a man should be masculine for him to qualify as a real man. This exercise has changed my way of thinking as I have learnt that; the men, women and everything that the media represents are all purposely meant to attract an audience. The advertisements, music, and films represent people who are a creation of fiction and not reality. Their representation in the media is more influenced by the need to paint a certain mental picture that only represents what should be ideal as opposed to what should be a reality. This essay on Mass Media Representation of Men and Women was written and submitted by user Saanvi I. to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.

Good People David Lindsay Abaire Essays

Good People David Lindsay Abaire Essays Good People David Lindsay Abaire Essay Good People David Lindsay Abaire Essay Some churches do that, but not SST. Vinci?s. And your grandmother had passed by then, so there was no dinner to go to. So your mother goes into Flagmans, and shes out to here. (indicates belly) Whens Jimmys birthday? STEVE January. Right, so shes out to here, and in this big coat. Remember that black coat she always wore? Yeah. And shes walking up and down the aisles, slipping things in the pockets potatoes, and Cans Of cranberry sauce, cookies, because you guys goat eat, right? So she gets whatever she can fit in that big coat, and comes waddling up to my register. And know somethings up, but I cant quite figure it out. So, Im like, Hey Mary, how are the kids? And she doesnt want talk obviously, shes just trying to push through the line, Oh, thefts good, I was just looking for something, but you dont have it, so Im goanna try someplace else. And then the ham falls out of her coat. It hits the floor right between her legs. A ham. Boom. And I swear to god, she didnt miss a beat. She looks up, real mad, and yells, Who threw that ham at me?! (really laughing now) : Oh, we died. Everybody there. Yaw had to laugh. Who threw that ham at me? She was a funny iconoclastic. Pardon my French. Look, Margaret-? Youre too young to remember. She never told you that story? (beat) No. God, she was funny. Think about her all the time. Your mother was a good lady. Its a lesson though. Youre lucky you dont smoke. Too young, your mother. You need some new posters, Steve. That one there is pretty corny. You should take it down. Cant. It comes with the office. Well, its corny. And very misleading. Know you dont want talk about why brought/ you in here No, I know. I was late, Im sorry. Its just, the district manager / comes in-? know. It was my Joyce again. You know I cant leave her alone when she gets auto sorts. And I pay Dot Leave a little bit to keep an eye on her, but Dots not the most reliable. Right, but the district manager comes down on me about it. No, I know, that guys an ass pardon my French, but thats what he is. Maybe, but hes also my boss. And he looks over those punch-cards. Okay. No, not okay. Youre late every day. Twenty, thirty minutes. Yesterday it was almost an hour. Its not every day. Pretty much it is, and that reflects badly on me. Pep my employees in line. He wants to know why I cant You have to explain about Joyce. I keep trying to get her into a program, but SSH?s too old for most fem.. And SSH?s not functional / enough to get a explained it to him, but theres only so much / we can-? Its not just me, Steve. Karen calls in sick every couple days. Yeah, well, Im talking to Karen next. Well, while youve got her in here , you should ask her why she tells everyone youre gay. What? She says youre gay. (more bemused than offended) Im not gay. Know. So why does she say that? Because you go to bingo. That makes me gay? m just saying what Karen says to people. You go to bingo a lot. More than I do. More than Karen does. Like bingo. Obviously. Plenty of men go to bingo. Wouldnt say plenty, butyrate. Freddy Gleason goes to bingo. Frank Moore. A few old-timers, but yeah, thats what I Eve been telling her. Okay, it doesnt matt-? Theses not what were talking about. Are you goanna bring it up with her though? No, that has nothing to do with-? Im going to say to her exactly what Im saying to you. The district manager came / in-? SSH?s late a lot more than I am. And she says youre gay. Margaret now youre not gay, and I tell her that, because youre dating whats her name. Dont know if thats supposed to be a secret, or whatever, but everybody knows that. Not Karen, obviously, but everybody knows that. Can you stop, please? Im talking about right now, and thats it The district manager came / in-? Okay, I understand. Ive been late, and wont be anymore. You can tell him I got the warning. No, this isnt a warning. Youve had warnings. / Ive given you You know I cant leave Joyce alone. You know that. Shes like a baby. And Dotted doesnt always show up when shes supposed to. So what am I / supposed to- Its not like I have a choice in this. If I dont let you go then I get fired. What do you mean, let me go? Told you it could happen. Now, come / on-? Every week the district manager comes in here to look at those punch-cards. Wont be late again. Tell him promise. Cover for you all the time, and he wont have it anymore. He wants me to let you go. Ill get somebody else to look after Joyce. Youve said that before. This is about the Chinese girl, isnt it. No, and shes not Chinese. She might be a little faster at the register, but she makes more mistakes. First of all, you know thats not true. / Secondly-? She lives two blocks away! Its easier for her to get here on time! Would listen to me? No, that guy comes in, and looks over your books, and whos getting paid what per hour-? Thats not what this is. And because Ive been here three years, I make a little bit more than the other girls, which costs the company a little bit more money Youre not reliable. You cant say that. I might be late once in a while but-? They dont want unreliable employees. This is a Dollar Store. Who do they think is goanna work here? Is that what I should tell them? What they dont want is someone making nine twenty an hour. And you know thats what this is. Ill talk to my brother. Maybe he can get you something down at Gillette. Gillette? Ill call him this afternoon. Thats just your way of getting me out the door. Ill call Jimmy, I swear to god. Not goanna call me in there. Besides, Ive been to Gillette, its a Fricke sweat-shop. No, it isnt, its a perfectly / fine-? Its all line-work. I cant work a line, Im too old for that. I cant keep up. Im trying to help you. You want help me, let me go back to my register. Its not my choice! Ill take a pay cut, Steve. No. A pay-cut? Margaret, listen to yourself. Know the Chinese girl gets eight sixty an hour, I can make do / on that. SSH?s from Thailand. Itll be tight, but can do eight sixty. Its not about what you get paid. That is bullwhip. Pardon my French, but that is bullwhip and you know it. I never asked for those raises. Only got them because you were required by law to give them to me. It wasnt much, god knows -a nickel here, fifteen cents one time -? but knew when went over nine dollars, you were goanna start looking for an excuse to get rid of me. STEVE You know thats not true. Well if not you, then the district manager was. Or whoever adds up the numbers. Why pay me when you can give minimum wage to Chow Fun? That doesnt help your case, Margaret. The racist stuff What racist stuff? Thats her name. (writes something down) You know thats not her name. What are you writing? You goanna put that in my file now? How Im a racist? Look, you wouldnt even be in here if you werent late. And I wouldnt be late if I didnt have to beg someone to watch my daughter, and I wouldnt have to beg someone if I could afford proper care! And Id have proper care if you didnt pay me a shut wage! Margaret-? Please, Steve. Last time got fired it took me seven months to find something, and that was when things werent so bad. Now? Forget it. I wont be able to find anything. Of course you will. You start asking around / and-? Eight fifteen. You can lower me to eight fifteen. Thats what I started at. Less what youd pay a new girl. Just pretend Im a new girl. I can do eight fifteen. Cant. I cant do that. Im sorry. Its just not working out. (pause) Youre lucky your mothers dead.

International trade Essay Example | Topics and Well Written Essays - 1500 words

International trade - Essay Example At the same time, least productive firms will be forced out and only produce for domestic market, this in turn will also lead to the exit of the least productive firms (Melitz, 2003). The Melitz model uses heterogeneous firms to perform its analysis under the general states of equilibrium. This concept explains how the exit of least productive firms leads to allocation of huge market shares to the well performing firms thus resulting into increase in productivity level. This indicates how certain firms are exposed to many opportunities by exit of the other non-performing ones. This paper also adapts the model for monopolistically competitive firms, that is, only highly competitive firms are given consideration under general equilibrium conditions. In addition, the concept suggests that uncertainty in production is a very important aspect and can help a great deal when trying to explain the behavior of firms. Uncertainty creates a business environment in which the players cannot predi ct the outcome of their competition and each firm therefore competes at its best. Also under this theory, there is an assumption that only the most productive firms that earn positive results remain in the competition. This analysis further puts focus on long run effects of this type of trade on performance and behavior of firms under different levels of productivity. Another very important aspect that this study emphasizes on is the introduction of dynamic future oriented market entry decisions by firms that are facing sunk costs of market entry. The study has focused on the importance of such market cost of such market entries and their effects on the firms’ competition. Description of the model The Melitz model focuses on three aspects to analyze its studies. These include demand, production, and aggregation. These aspects are relative and are majorly the key determinants in decision-makings. Demand Demand relates to consumer preferences. The preferences relating to the re presentative of the consumer can be got by CES utility function all over a range of goods that is represented by the company. The function below can be used in this analysis (w)q dw]1/p ? represent the value of the mass of the goods available. The available goods are considered a substitute which implies that P is less than 1, but greater than 0. 0 1 Consumer behavior in regards to demand can be analyzed by considering a set of products that the consumer takes against the aggregate price of the commodities. The presence of simultaneous entry and exit during the state of steady equilibrium can be attributed to the sunken market entry cost. It also explains the survival probabilities of exporting firms in the market (Johnson, 2010). These aggregates can be applied in deriving optimal consumption and decisions regarding expenditure of various individuals. Production The industry has many firms, and each of these firms chooses to produce variety of products w. the production process is viewed to require only one important factor, that is labor. The factor of production labor L is in-elastically available at its total level. The technological level of these firms is represented by cost functions hat show constant marginal cost that is characterized by fixed overhead cost. Labor used can therefore be represented using linear function for output q i.e 1= f + q/q. An assumption that all firms share similar fixed cost f

Analyse a case of Georgia v Russian Federation Essay

Analyse a case of Georgia v Russian Federation - Essay Example At this time both Georgia and South Ossetia were basically Christian territories. As the time passed by, the increased intermarriages between residents of Georgia and South Ossetia resulted in emergence of autonomous groups which were multi-ethnic (Hafkin, 2009, pp. 222). The attack by Russia in 1990s ruined these relations and heightened political unrest between Russia and Georgia that would further result into a series of incidents that resulting into loss of lives, displacement of people and massive destruction of properties (Nichol, 2008, pp. 4-5). In the case, the question of which of the two states invaded the territories of the other and for what reasons has been a hard one to define. Historically, South Ossetia declared itself an independent state from Georgia in 1990s, this is the incident that led into a civil war between Georgia and South Ossetia militants that lasted for at least three years and led to thousands of deaths and thousands of displacements before an agreement was reached by these two nations in 1992 (Nichol, 2008, pp. 4; Hafkin, 2009, pp. 222). Was Georgia responsible for these deaths or was it Russia? Russia had a stake in the 1991 war since Russia provided South Ossetia with the necessary arms and mercenaries to fight Georgia. Who was responsible for the deaths and displacement of thousand people? Both Russia and Georgia fully participated in war in one way or another. This did not end here. After reaching a peace agreement between the three states, there still existed tensions between Russia and Georgia . People from Georgia and Ossetia had to live with mistrust amongst them for Georgia viewed South Ossetia as a tool of Russians to destroy them (Nichol, 2008, pp. 5). According to Toal (2008, pp. 1-2), it is clearly evident that Russia led South Ossetia into a retaliatory fight against Georgia that resulted into numerous damage all over Georgian State. This conflict

Assignment Example | Topics and Well Written Essays - 750 words - 118

Assignment Example The banking system has played an important role in home mortgages due to their role that has grown in turn in home mortgages to securities. The chain involved in securitization starts with origination of mortgages and sold to one or more financial entities before they end up to mortgage loans that are sold to investors. The value of security obtain is related to value of mortgage loans that are used to back up security paid with interest. The backed up security is paid in interest and those that own homes pay the mortgage loans. The process and stages that shadow banking is involved leads to generation of finance. This makes the process essential in generation of finances. There are differences and similarities that occur in the banks. In similarity, both the banks are seen to perform credit intermediation. However, there are varied differences that occur in the banks. In convention al banks, there is occurrence under the same roof while in shadow banking, giving out of credits occurs through a chain of entities. This makes operation in shadow banking to be more complex than in conventional banks. Another difference that can be noted between the two banks is on the regulations that are involved in acquiring credit. Conventional banks are strictly regulated having access to central bank funding and deposit insurance schemes. In shadow banks, there is little or no regulation since they are not able to access funding from central banks. Shadow banking is also based on wholesale funding. The difference that the banks have on the source of funding also is brought out in conventional banking. Universal banking refers to the condition in which are allowed to give a variety of services to their customers. The banks are not just restricted to provision of services related to loans and savings but also involved in in other services such as investments. In baking category there is banking which considers different aspects.

Information Retrieval from Large Databases: Pattern Mining

Information Retrieval from Large Databases: Pattern Mining Efficient Information Retrieval from Large Databases Using Pattern Mining Kalaivani.T, Muppudathi.M Abstract With the widespread use of databases and explosive growth in their sizes are reason for the attraction of the data mining for retrieving the useful informations. Desktop has been used by tens of millions of people and we have been humbled by its usage and great user feedback. However over the past seven years we have also witnessed some changes in how users store and access their own data, with many moving to web based application. Despite the increasing amount of information available in the internet, storing files in personal computer is a common habit among internet users. The motivation is to develop a local search engine for users to have instant access to their personal information.The quality of extracted features is the key issue to text mining due to the large number of terms, phrases, and noise. Most existing text mining methods are based on term-based approaches which extract terms from a training set for describing relevant information. However, the quality of the extract ed terms in text documents may be not high because of lot of noise in text. For many years, some researchers make use of various phrases that have more semantics than single words to improve the relevance, but many experiments do not support the effective use of phrases since they have low frequency of occurrence, and include many redundant and noise phrases. In this paper, we propose a novel pattern discovery approach for text mining.To evaluate the proposed approach, we adopt the feature extraction method for Information Retrieval (IR). Keywords –Pattern mining, Text mining, Information retrieval, Closed pattern. 1.Introduction In the past decade, for retrieving an information from the large database a significant number of datamining techniques have been presented that includes association rule mining, sequential pattern mining, and closed pattern mining. These methods are used to find out the patterns in a reasonable time frame, but it is difficult to use the discovered pattern in the field of text mining. Text mining is the process of discovering interesting information in the text documents. Information retrieval provide many methods to find the accurate knowledge form the text documents. The most commonly used method for finding the knowledge is the phrase based approaches, but the method have many problems such as phrases have low frequency of occurrence, and there are large number of noisy phrases among them.If the minimum support is decreased then it will create lot of noisy pattern 2.Pattern Classification Method To find the knowledge effectively without the problem of low frequency and misinterpretation a pattern based approach(Pattern classification method) is discovered in this paper. This approach first find out the common character of pattern and evaluates the weight of the terms based on distribution of terms in the discovered pattern. It solves the problem of misinterpretation. The low frequency problem can also be reduced by using the pattern in the negatively trained examples. To discover patterns many algorithms are used such as Apriori algorithm, FP-tree algorithm, but these algorithms does not tell how to use the discovered patterns effectively. The pattern classification method uses closed sequential pattern to deal with large amount of discovered patterns efficiently. It uses the concept of closed pattern in text mining. 2.1 Preprocessing The first step towards handling and analyzing textual data formats in general is to consider the text based information available in free formatted text documents.Real world databases are highly susceptible to noisy, missing, and inconsistent data due to their huge size. These low quality data will lead to low quality mining results. Initially the preprocessing is done with text document while storing the content into desktop systems.Commonly the information would be processed manually by reading thoroughly and then human domain experts would decide whether the information was good or bad (positive or negative). This is expensive in relation to the time and effort required from the domain experts. This method includes two process. 2.1.1 Removing stop words and stem words To begin the automated text classification process the input data needs to be represented in a suitable format for the application of different textual data mining techniques, the first step is to remove the un-necessary information available in the form of stop words.Stop words are words that are deemed irrelevant even though they may appear frequently in the document. These are verbs, conjunctions, disjunctions and pronouns, etc. (e.g. is, am, the, of, an, we, our). These words need to be removed as they are less useful in interpreting the meaning of text. Stemming is defined as the process of conflating the words to their original stem, base or root. Several words are small syntactic variants of each other since they share a common word stem. In this paper simple stemming is applied where words e.g. ‘deliver’, ‘delivering’ and ‘delivered’ are stemmed to ‘deliver’. This method helps to capture whole information carrying term space and also reduces the dimensions of the data which ultimately affects the classification task. There are many algorithms used to implement the stemming method. They are Snowball, Lancaster and the Porter stemmer. Comparing with others Porter stemmer algorithm is an efficient algorithm. It is a simple rule based algorithm that replaces a word by an another. Rules are in the form of (condition)s1->s2 where s1, s2 are words. The replacement can be done in many ways such as, replacing sses by ss, ies by i, replacing past tense and progressive, cleaning up, replac ing y by i, etc. 2.1.2 Weight Calculation The weight of the each term is calculated by multiplying the term frequency and inverse document frequency. Term frequency find the occurrence of the individual terms and counts. Inverse document frequency is a measure of whether a term is common or rare across all documents. Term Frequency: Tf(t,d)=0.5+0.5*f(t,d)/max{f(w,d):wbelongs to d} Where d represents single document and t represents the terms Inverse Document Frequency: IDF(t,D)= log(Total no of doc./No of doc. Containing the term) Where D represents the total number of documents Weight: Wt=Tf*IDF 2.2 Clustering Cluster is a collection of data objects. Similar to one another within the same cluster. Cluster analysis will find similarities between data according to the characteristics found in the data and grouping similar data objects into clusters.Clustering is defined as a process of grouping data or information into groups of similar types using some physical or quantitative measures. It is an unsupervised learning. Cluster analysis used in many applications such as, pattern recognition, data analysis and web for information discovery. Cluster analysis support many types of data like, Data matrix, Interval scaled variables, Nominal variables, Binary variables and variables of mixed types. There are many methods used for clustering. The methods are partitioning methods, hierarchical methods, density based methods, grid based methods and model based methods. In this paper partitioning method is proposed for clustering. 2.2.1 Partitioning methods This method classifies the data into k-groups, which together satisfy the following requirements: (1) each group must contain at least one object, (2) each object must belong to exactly one group. Given a database of n objects, a partitioning method constructs k partitions of the data, where each partition represents a cluster and k 2.2.2 K-means algorithm K-means is one of the simplest unsupervised learning algorithms. It takes the input parameter, k, and partitions a set of n objects into k-clusters so that the resulting intra cluster similarity is high but the inter cluster similarity is low. It is centroid based technique. Cluster similarity is measured in regard to the mean value of the objects in a cluster, which can be viewed as the clusters centroid. Input:k: the number of clusters, D: a data set containing n objects. Output: A set of k clusters. Methods: Select an initial partition with k clusters containing randomly chosen samples, and compute the centroids of the clusters. Generate a new partition by assigning each sample to the closest cluster center. Compute new cluster centers as the centroids of the cluster. Repeat steps 2 and 3 until an optimum value of the criterion function is found or until the cluster membership stabilizes. This algorithm faster than hierarchical clustering. But it is not suitable to discover clusters with non-convex shapes. Fig.1. K-Means Clustering 2.3 Classification It predicts categorical class labels and classifies the data based on the training set and the values in classifying the attribute and uses it in classifying the new data. Data classification is a two step process (1) learning, (2) classification. Learning can be classified into two types supervised and unsupervised learning. The accuracy of a classifier refers to the ability of a given classifier to correctly predict the class label of new or previously unseen data. There are many classification methods are available such as, K-nearest neighbor, Genetic algorithm, Rough Set Approach, and Fuzzy Set approaches.The classification technique measures the nearing occurrence. It assumes the training set includes not only the data in the set but also the desired classification for each item. The classification is done through training samples, where the entire training set includes not only the data in the set, but also the desired classification for each item. The Proposed approaches find the minimum distance from the new or incoming instance to the training samples. On the basis of finding the minimum distance only the closest entries in the training set are considered and thenew item is placed into the classwhich contains the most items of the K. Here classify thesimilarity text documents and file indexing is performed to retrieve the file in effective manner. 3. Result and Discussion The input file is given and initial preprocessing is done with that file. To find the match with any other training sample inverse document frequency is calculated. To find the similarities between documents clustering is performed.Then classification is performed to find the input matches with any of the clusters. If it matches the particular cluster file will be listed.Theclassification techniques classify the various file formats and the report is generated as percentage of files available. The graphical representation shows the clear representation of files available in various formats. This method uses least amount of patterns for concept learning compare to other methods such as, Rocchio, Prob, nGram , the concept based models and the most BM25 and SVM models. The proposed model is achieved the high performance and it determined the relevant information what users want. This method reduces the side effects of noisy patterns because the term weight is not only based on term spac e but it also based on patterns. The proper usage of discovered patterns is used to overcome the misinterpretation problem and provide a feasible solution to effectively exploit the vast amount of patterns generated by data mining algorithms. 4. Conclusion Storing huge amount of files in personal computers is a common habit among internet users, which is essentially justified for the following reasons, 1) The information will not always permanent 2) The retrieval of information differs based on the different query search 3) Location same sites for retrieving information is difficult to remember 4) Obtaining information is not always immediate. But these habits have many drawbacks. It is difficult to find when the data is required.In the Internet, the use of searching techniques is now widespread, but in terms of personal computers, the tools are quite limited. The normal â€Å"Search or â€Å"Find† options take several hours to produce the search result. It acquires more time to predict the desire result where the time consumption is high.The proposed system provides accurate result comparing to normal search.All files are indexed and clustered using the efficient k means techniques so the information retrieved in efficient manner. The best and advanced clustering gadget provides optimized time results.Downtime and power consumption is reduced. 5.References [1]K. Aas and L. Eikvil, ‘’Text Categorization: A Survey,’’ Technical Report NR 941, Norwegian Computing Centre, 1999. [2] R. Agarwal and R.Srikanth, ‘’Fast Algorithm for Mining Association Rules in Large Databases, ‘’ Proc. 20th Int’l Conf. Very Large Data Bases(VLDB’94), pp.478-499, 1994. [3] H. Ahonen, O. Heinonen, M. Klemettinen, and A.I. Verkamo, â€Å"Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collections,† Proc. IEEE Int’l Forum on Research and Technology Advances in Digital Libraries (ADL ’98), pp. 2-11, 1998. [4] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison Wesley, 1999. [5] N. Cancedda, N. Cesa-Bianchi, A. Conconi, and C. Gentile, â€Å"Kernel Methods for Document Filtering,† TREC, trec.nist.gov/ pubs/trec11/papers/kermit.ps.gz, 2002. [6] N. Cancedda, E. Gaussier, C. Goutte, and J.-M. Renders, â€Å"Word- Sequence Kernels,† J. Machine Learning Research, vol. 3, pp. 1059- 1082, 2003. [7] M.F. Caropreso, S. Matwin, and F. Sebastiani, â€Å"Statistical Phrases in Automated Text Categorization,† Technical Report IEI-B4-07- 2000, Instituto di Elaborazionedell’Informazione, 2000. [8] C. Cortes and V. Vapnik, â€Å"Support-Vector Networks,† Machine Learning, vol. 20, no. 3, pp. 273-297, 1995. [9] S.T. Dumais, â€Å"Improving the Retrieval of Information from External Sources,† Behavior Research Methods, Instruments, and Computers, vol. 23, no. 2, pp. 229-236, 1991. [10] J. Han and K.C.-C. Chang, â€Å"Data Mining for Web Intelligence,† Computer, vol. 35, no. 11, pp. 64-70, Nov. 2002. [11] J. Han, J. Pei, and Y. Yin, â€Å"Mining Frequent Patterns without Candidate Generation,† Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD ’00), pp. 1-12, 2000. [12] Y. Huang and S. Lin, â€Å"Mining Sequential Patterns Using Graph Search Techniques,† Proc. 27th Ann. Int’l Computer Software and Applications Conf., pp. 4-9, 2003. [13] N. Jindal and B. Liu, â€Å"Identifying Comparative Sentences in Text Documents,† Proc. 29th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’06), pp. 244-251, 2006. [14] T. Joachims, â€Å"A Probabilistic Analysis of the Rocchio Algorithm with tfidf for Text Categorization,† Proc. 14th Int’l Conf. Machine Learning (ICML ’97), pp. 143-151, 1997. [15] T. Joachims, â€Å"Text Categorization with Support Vector Machines: Learning with Many Relevant Features,† Proc. European Conf. Machine Learning (ICML ’98),, pp. 137-142, 1998. [16] T. Joachims, â€Å"Transductive Inference for Text Classification Using Support Vector Machines,† Proc. 16th Int’l Conf. Machine Learning (ICML ’99), pp. 200-209, 1999. [17] W. Lam, M.E. Ruiz, and P. Srinivasan, â€Å"Automatic Text Categorization and Its Application to Text Retrieval,† IEEE Trans. Knowledge and Data Eng., vol. 11, no. 6, pp. 865-879, Nov./Dec. 1999. [18] D.D. Lewis, â€Å"An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task,† Proc. 15th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’92), pp. 37-50, 1992. [19] D.D. Lewis, â€Å"Feature Selection and Feature Extraction for Text Categorization,† Proc. Workshop Speech and Natural Language, pp. 212-217, 1992. [20] D.D. Lewis, â€Å"Evaluating and Optimizing Automous Text Classification Systems,† Proc. 18th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’95), pp. 246-254, 1995. [21] G. Salton and C. Buckley, â€Å"Term-Weighting Approaches in Automatic Text Retrieval,† Information Processing and Management: An Int’l J., vol. 24, no. 5, pp. 513-523, 1988. [22] F. Sebastiani, â€Å"Machine Learning in Automated Text Categorization,† ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002. [23] Y. Yang, â€Å"An Evaluation of Statistical Approaches to Text Categorization,† Information Retrieval, vol. 1, pp. 69-90, 1999. [24] Y. Yang and X. Liu, â€Å"A Re-Examination of Text Categorization Methods,† Proc. 22nd Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’99), pp. 42-49, 1999. : .

William Goldings Lord of the Flies Lord Of The Flies: Piggy, Ralph, Ja

Lord Of The Flies is possibly one of the most complex novels of the twentieth century. This complexity and depth is evident when the characters are compared to the psychological teachings of Freud. The book shows examples of this psyche in the characters Jack, Piggy and Ralph and how they change during their time on the island. Towards the end of the eighth chapter it became very apparent that Piggy and Jack both had two very different ideas on how they would survive. Jack thinks that hunting and having fun is key, Jack is more worried with instant gratification and doesn’t worry about what will happen off the island he worries about having fun and living on the island. Piggy is only concerned with keeping the fire lit and getting off the island. Unlike Jack, Piggy believes more in thinking about the future, how they will be saved and how they can endure the time they are on the island. When the fire goes out Piggy cries out at Jack, who was in charge of the fire, â€Å" You and your blood, Jack Merridew! You and your hunting! We might have gone home.† This shows the extent of Piggy’s will to be rescued. As a result of these major differences Jack decides to head down the beach and build a new tribe. He tells the others on the island that with his new clan â€Å"we hunt and feast and have fun†¦Ã¢â‚¬  (Chap. 8 p140) by announcing this he appeals to the childish more uncivilized collection of the kids. The boys recognized that Jack was a stronger and more self-sufficient chief so many ch...