Sentiment Analysis: A Survey <s> CONCLUSION <s> Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST. <s> BIB001 </s> Sentiment Analysis: A Survey <s> CONCLUSION <s> We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability. <s> BIB002
There are various methods to classify text. Several techniques involves word and phrase classification methods exist. Many Lexical and dictionary based approach BIB002 are used and have been fairly Successful some of them even showed more than 70 percent accuracy on standard data sets. Another approach that is more commonly used is Machine Learning. Various Machine learning techniques have been tried and used in the papers mentioned. The most common ones are: Naive Bayesian, Support Vector Machine  and Maximum entropy. The success of a machine learning algorithm depends upon how well the features are selected and extracted. Various different features have been used, the ones that are most commomn are Unigrams, Parts of speech tagging, emoticons detection, categories tagging, using graph based techniques. Topic modelling BIB001 and phases based features  are also used. .