Title: Sentiment Identification in Code-Mixed Social Media Text,
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.