This paper proposes a rule based approach for sentiment analysis from Malayalam movie reviews. The research in Sentiment Analysis nowadays become one among active research areas in natural language processing. Sentiment Analysis is the cognitive process in which the user's feeling and emotions are extracted. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, and social networks. Sentiment analysis enables computers to automate the activities performed by human for making decisions based on the sentiment of opinions, which has wide applications in data mining, web mining, and text mining. Negation Rule has been applied for extracting the Sentiments from a given text. This system gives the polarity at the sentence level for the movie reviews with an accuracy of 85%, when analysed.

The unprecedented growth of data in web, social media and the attempt to make the cognitive process using computers make Sentiment Analysis a challenging and interesting research problem. Sentiment Analysis mainly deals with the process of analyzing the sentiments or feelings from someone's expression or piece of information, and also in discovering the cognitive behavior of humans. The usage of computers to get feedback, opinion or remarks about a product, entertainment or political view of the public is very common. This paper demonstrates how Sentiment Analysis can be used in reviewing Malayalam films by using machine learning techniques. It is a hybrid approach comprising of machine learning techniques and rule based approach. This work would help the users to analyze the film criticism and also to assign the rank and popularity of new arrival films . In this work, the system checks the polarity at the sentence level, resulted in an accuracy of 91%.

Natural Language Processing (NLP) is both a modern computational technology and a method of investigating and evaluating claims about human language itself. Some prefer the term Computational Linguistics in order to capture this latter function, but NLP is a term that links back into the history of Artificial Intelligence (AI), the general study of cognitive function by computational processes, normally with an emphasis on the role of knowledge representations, that is to say the need for representations of our knowledge of the world in order to understand human language with computers. A morphological analyzer or generator supplies information concerning morphosyntactic properties of the words it analyses or constructs.Morphological Analysis and Generation are important components for building computational grammars as well as Machine Translation. Morphological Analyzer is a program for analyzing the morphology of an input word; the analyzer reads the inflected surface form of each word in a text and provides its lexical form while Generation is the inverse process. Both Analysis and Generation make use of lexicon.Malayalam like the other languages in the Dravidian family exhibits the characteristics of an agglutinative language. Here using a bilingual dictionary, the Malayalam morphological analyzer and the Tamil morphological generator have been described.

Word Sense Disambiguation (WSD) aims to classify each ambiguous word in a particular context with a set of predefined classes. The sense of a specific word in the particular context gives a significant amount of information about the word and its neighbours which can be useful in a language model for different speech and text processing applications. There have been a number of major advances in WSD for many languages, from dictionary-based methods to supervised learning methods and unsupervised learning. This paper describes a hybrid approach using a multi-class SVM and corpus based to Malayalam word sense tagging. This framework makes use of the contextual feature information along with the parts of speech tag feature in order to predict the various WSD classes. For training set, limited number of ambiguous words has been annotated with 16 WSD classes. The experimental results of the 10 fold cross validation shows the appropriateness of the proposed multi-class SVM of Malayalam word sense tagger with one against one approach for both word only and word +POS.