Sentiment Identification in Code-Mixed Social Media Text

TL;DR

This paper introduces a novel approach for sentiment analysis of code-mixed social media text, specifically Facebook posts, using machine learning, and presents a new labeled corpus for this task.

Contribution

It is the first work to perform sentiment analysis on code-mixed social media data, utilizing extensive preprocessing and a multilayer perceptron model.

Findings

Successfully classified positive and negative sentiments in code-mixed Facebook posts.

Developed and manually labeled a new corpus for sentiment analysis in code-mixed social media text.

Achieved promising results demonstrating feasibility of machine learning for this task.

Abstract

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is a presence of sentiment in the text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach.While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labeling Facebook posts with their associated sentiments.