A SUBJECTIVE FEATURE EXTRACTION FOR SENTIMENT ANALYSIS IN MALAYALAM LANGUAGE

In recent days, Sentiment Analysis has become an active research in NLP, which analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from writing language. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, and social network. In his paper, sentiment analysis of Malayalam film review is carried out using machine learning techniques CRF combined with a rule based approach. The system shows 82 % accuracy. INTRODUCTION Sentiment Analysis (SA) is a process that helps to extract the subjective or conceptual information from various sources. It deals with analyzing emotions, feelings and the attitude of a speaker or a writer from a given piece of text. In a broader sense, SA is a cognitive process which helps computer to understand and extract human behavior such as likes and dislikes, feelings and emotions and many other attributes and also to predict the behavioral aspects of human. It is also used for opinion mining, one of the hottest topics in NLP that helps to identify and extract subjective information in source materials and provides valuable insights about the user’s intentions, taste and likeliness etc. Facts and opinions are two main types of textual information in the world. Facts are objective expressions about entities, events and their properties while opinions are usually subjective expressions that describe people’s sentiments, feelings toward entities, events and their properties. Opinion expressions convey people’s positive or negative sentiments and it may be a neutral comment. Present research on textual information processing has been focused on mining and retrieval of factual information. The web has dramatically changed the way that people express their views and opinions. Common men can now post reviews of products at various online product review sites and express their views on almost anything in Internet forums, discussion groups, social media and blogs, which are collectively called the user-generated content. This would lead to measurable sources of information, which helps to improve the quality of the product and for a better feedback and choice to the user. Such system can be further modified to an automatic textual analysis for sentiments, automatic survey analysis, opinion extraction, or a recommender system. Such system typically tries to extract the overall sentiment revealed in a sentence or document, either positive or negative, or neutral. Malayalam belongs to the Dravidian family, a large family of languages of South and Central India, and SriLanka. Malayalam exhibits heavy amount of agglutination. Due to the agglutination and rich morphology of words along with high ambiguity of Malayalam language, research in NLP for Malayalam is always challenging and is same for sentiment analysis because of its high dependence on words that are used for expressing the feelings or other sentiments. The sentence-level and document-level review has been considered. Focus on the sentence-level sentiment extraction is significant because in most of the websites, user comments are just a single sentence. Document -level provides the semantics of the entire document, but often fails to detect sentiment about individual aspects of the topic. A statistical approach using simple co-occurrence that commonly used machine learning techniques is a trivial approach, but fail to provide a better result, especially in cases where both negative and positive comes in two differ sentences in a document. In order to resolve such shortcoming, we propose a hybrid statistical model using rule based and extracting the grammatical features. The paper is organized into different sections. First section dealt with the introduction about SA and the objective of the paper. The second section exposed the states of the art that provides some of the major work carried out in this area. The third section reveals the proposed work and the methodologies. The fourth section includes the implementation and the result obtained. The fifth section concludes the paper. STATES OF THE ART SENTIMENT ANALYSIS There has been a wide range of work carried out on this topic. The main research carried out in the area of sentiment analysis is in the document and sentence level. Document and sentence level classification methods are usually based on the classification of review context or words. Most of the work done is by using either of these three methods, Semantic Orientation method, Machine Learning method or Rule Based approach. One of the first attempts in this field was done by Alekh Agarwal and Pushpak Bhattacharyya [1] for English. In this paper they made an attempt to determine the overall polarity of a document, such as identifying for the appreciation or criticism of a movie. They presented machine learning based approach to solve the problem of determining the sentiments similar to text categorization. The movie review was selected for their experiments. Their paper concluded with an accuracy of over 90% for the first time. Another work on the sentiment extraction of movie was done by Pang [2]. The ultimate aim of that work was to find the best way to classify the sentiment from text, either standard machine learning techniques or human-produced baseline. Three different machine learning techniques explained were mainly Maximum Entropy, Support Vector Machine, and Naive Bayes. In their experiment, they tried different variations of n-gram approach like unigrams presence, Jisha P. Jayan, Deepu S. Nair, Elizabeth Sherly Research Cell : An International Journal of Engineering Sciences, Special Issue March 2015, Vol. 14 ISSN: 2229-6913 (Print), ISSN: 2320-0332 (Online) -, Web Presence: http://www.ijoes. vidyapublications.com © 2014 Vidya Publications. Authors are responsible for any plagiarism issues. 2 unigrams with frequency, unigrams with bigrams, bigrams, unigrams with POS, adjectives, most frequent unigrams, unigrams with positions They concluded that machine learning techniques are quite good in comparison to the human generated baseline. The paper also remarked that the Naïve Bayes approach tend to do the worst while SVM performs the best. Manurung, and Ruli [3] work was carried out in 2008 for Indonesian Language using machine learning method. In this work, he initially translated English movie review into Indonesian language and then applied to the machine learning approach such as Naive Bayes, SVM, and maximum Entropy method to perform the sentiment classification. He reached at the conclusion that SVM is the best classification method giving 80.09% accuracy. Saggion and Funk [4] used senti-wordnet to perform opinion classification. They calculated positive and negative score for a review and based on the maximum score, the polarity of the review was assigned. They also extracted features and used machine learning algorithms to perform classification of the sentiments from the text. Turney [5] also worked on part of speech (POS) information. He used tag patterns with a window of maximum three words using trigrams. In his experiment, he considered JJ, RB, NN, NNS POS-tags with some set of rules for classification of product reviews. He used adjectives and adverbs for performing opinion classification on reviews. PMI-IR algorithm is used to estimate the semantic orientation of the sentiment phrase. He achieved an average accuracy of 74% on 410 reviews of different domains collected from opinion. Barbosa [6] designed a 2-step automatic sentiment analysis method for classifying tweets. They used a noisy training set to reduce the labelling effort in developing classifiers. First, they classified tweets into subjective and objective tweets. Then subjective tweets are classified as positive and negative tweets. Celikyilmaz [7] design a pronunciation based word clustering method for tweet normalization. In pronunciation based word clustering, words having similar pronunciation are clustered and assigned common tokens. They also used text processing techniques like assigning similar tokens for numbers, html links, user identifiers, and target organization names for normalization. After doing normalization, they used probabilistic models to identify polarity lexicons. They performed classification using the BoosTexter classifier with these polarity lexicons as features and obtained a reduced error rate. In Malayalam, the works on sentiment analysis is in its infant stage. Geethu Mohandas [8], had proposed a semantic orientation method for extraction of the mood from any sentence. In their study, they have applied the semantic orientation method using an unsupervised learning technology for classifying the input text for classification. They used a tag set which includes the tags sorrow, joy, anger and neutral for tagging the manually created corpus and then calculated the semantic orientation by semantic association using SO-PMI (Semantic Orientation from Point wise Mutual Information). They concluded their paper with a conclusion that the SO-PMI method gives about 63% accuracy. PROPOSED WORK The proposed work concentrates on sentiment analysis to find the positive, negative or neutral opinions from the user’s writings at the document level. The polarity of sentence and rating of individual category, such as film, direction, acting, song, script etc. is individually computed. The different suggestions, opinion and feedback about the film by considering different factors improve the overall ranking in a more meaningful manner and also item wise scoring. This work has been implemented on a hybrid approach combining the machine learning technique with rules. Since Malayalam is a highly agglutinative language with rich morphology, and also of free order, it has a wide range of fluctuated words with the same meaning. Also such reviews, there are a number of colloquial usages, short forms and broken sentences. Here we used Conditional