“Sentiment Analysis of Code-Mixed Social Media Text in Indian-Languages”
Keywords:
Natural Language Processing, Machine Learning, Sentiment Analysis, Code-mixed, Datasets, Language Identification, Named Entity RecognitionAbstract
The arrival of web 2.0 platforms and increasing usage of social networking sites have proliferated social media content on the web. These platforms also provides multilingual interface to allow people to write freely in their native language.Over the past few decades, a new phenomenon called “code-mixing” has been observed in social media data which has attracted attention of researchers in sociolinguists and Natural Language Processing domains. However, due to informal nature of the text present in code-mixing phenomenon, there are a number of challenges ranging from data extraction to summarization. Sentiment Analysis of code-mixed data is one of the key research field which has emerged in recent past. Sentiment Analysis is the combination of application areas such as Natural Language Processing, Statistical methods and linguistics that classify a document or a sentence into positive, negative and neutral categories. These classes represent opinions/views of a person about a product, service, an event, a social movement, a political issue or a government policy. Extracting such useful information from unstructured data has applications in business, marketing, commerce, travel, finance, healthcare, politics etc. The goal is to provide natural language processing (NLP) tools that can collect, analyzed, evaluate, and summarize CM (“code-mixed”) data. The researchers had to deal with dataset construction, preprocessing, annotation, language identification, feature extraction and feature selection, and sentiment classification when it came to sentiment analysis of CMSMT (“Code-mixed Social Media Text”). This paper provides a detailed overview of work carried out in these challenges which will help researchers of this field in their future directions.
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