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There is an increasing number and variety of research papers in the area of sentiment analysis and classification We chose to consider only related work which makes use of the following machine learning techniques document-level classification n-gram features such as unigrams part-of-speech tagging lexical resources especially SentiWordNet sentiment classification techniques involving support vector machines SVM and Nave Bayes and the review domain such as movie reviews or product reviews We did not consider other research work which used natural language processing techniques did not involve feature selection and did not utilize SVM and Nave Bayes classifiers32 Review of LiteraturePang and Lee applied machine learning techniques to classify movie reviews according to sentiment 21 They employed Naive Bayes Maximum Entropy and SVM classifiers and observed that these do not perform as well on sentiment classification tasks as on traditional topic-based text classification tasks However SVM did generate a higher accuracy than Nave Bayes and Maximum Entropy They noticed that using unigrams as features with term presenceon SVM always yielded highly accurate results but when bigrams were used as features the accuracy was lower as compared to that of the unigrams They also found out that machine learning techniques outperformed human-produced baselinesIn other work they attempted to improve the classifiers by using only the subjective sentences in movie reviews 13 They explored extraction methods based on a minimum cut formulation framework which resulted in the development of efficient algorithms for sentiment analysis They noted that utilizing contextual information via this framework can lead to a statistically significant improvement in polarity classification accuracyAn approach to sentiment analysis using SVMs and a variety of diverse information sources was introduced by Mullen and Collier 12 Their work involved extracting value phrases two word phrases conforming to a particular part of speech and assigning them sentiment orientation values using pointwise mutual information They used SVM in conjunction with other hybrid models of SVM to classifier their datasets Their results indicated that
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