Sentiment analysis of malayalam tweets using machine learning techniques

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%.

Sentiment Analysis of Malayalam film review using machine learning techniques

The enormous number of user activity on online social networks results in a considerable amount of data which expresses the opinion from millions of people with diversity in their social aspects. The freedom of language usage shared through social media paves the way for the existence of code-mixed data that turns out to be more complex for mining the information out of it. Considering this, we created Kannada-English code mixed corpus by crawling Facebook comments. As of now, there is no relevant corpus as well as literature available for code-mixed Kannada-English sentiment analysis. In addition to the crawled corpus, we also used sentiment analysis code-mixed corpus provided by Sentiment Analysis for Indian Languages (SAIL)-2017 which includes Bengali-English and Hindi-English languages. This paper also addresses the performance of distributed representation methods in sentiment analysis task. We have reported comparisons among different machine learning and deep learning techniques.

Sentiment Analysis for Code-Mixed Indian Social Media Text With Distributed Representation

In recent years, due to the availability of voluminous data on web for Indian languages, it has become an important task to analyze this data to retrieve useful information. Because of the growth of Indian language content, it is beneficial to utilize this explosion of data for the purpose of sentiment analysis. This research depicts a systematic review in the field of sentiment analysis in general and Indian languages specifically. The current status of Indian languages in sentiment analysis is classified according to the Indian language families. The periodical evolution of Indian languages in the field of sentiment analysis, sources of selected publications on the basis of their relevance are also described. Further, taxonomy of Indian languages in sentiment analysis based on techniques, domains, sentiment levels and classes has been presented. This research work will assist researchers in finding the available resources such as annotated datasets, pre-processing linguistic and lexical resources in Indian languages for sentiment analysis and will also support in selecting the most suitable sentiment analysis technique in a specific domain along with relevant future research directions. In case of resource-poor Indian languages with morphological variations, one encounters problems of performing sentiment analysis due to unavailability of annotated resources, linguistic and lexical tools. Therefore, to provide efficient performance using existing sentiment analysis techniques, the aforementioned issues should be addressed effectively.

A journey of Indian languages over sentiment analysis: a systematic review

What is the Lexicon

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.

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Morphological Analyser and Morphological Generator for Malayalam Tamil Machine Translation

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.

SentiMa - Sentiment extraction for Malayalam

Anaphora resolution (AR) is the process of resolving references to an entity in the discourse The paper presents an algorithm to identify the pronominals and its antecedents in the Malayalam text input Anaphora resolution is achieved by employing a hybrid of statistical machine learning and rule based approaches The system is implemented by exploiting the morphological richness of the language and it makes use of parts of speech tagging, subject-object identification and person-number-gender of the NPs We outline a simple, efficient but a naive algorithm for anaphora resolution, which computes the salience value score for each antecedents The system performance is evaluated with precision, recall measures which produced promising results The anaphora resolution system itself can improve the performance of many NLP applications such as text summarisation, text categorisation and term extraction

R. R. Rajeev

Papers

Morphological Analyser and Morphological Generator for Malayalam Tamil Machine Translation

SentiMa - Sentiment extraction for Malayalam

Pronominal anaphora resolution using salience score for Malayalam