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.
Identifying the semantic polarity (positive vs. negative connotation) of words has been done using different approaches. Some of the works (knowledge-based) explicitly attempted to find features indicating that subjective language is being used. @cite_1 made use of corpus statistics, @cite_26 used linguistic tools such as WordNet @cite_24 , and @cite_7 used lexicon-based classifier. @cite_6 work on classification of reviews was based on using an unsupervised learning technique. They found the mutual information between document phrases and the words like “excellent” and “poor”. The mutual information was computed using statistics gathered by a search engine. In their work on automatic classification of sentiment in online domains, @cite_15 evaluated the performance of different classifiers on movie reviews. They demonstrated that that standard machine learning techniques outperform human-produced baselines.
@cite_26 @cite_7 @cite_1 @cite_6 @cite_24 @cite_15
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Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarity and gradability (Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone. This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces. Current WordNet-based measures of distance or similarity focus almost exclusively on WordNet’s taxonomic relations. This effectively restricts their applicability to the syntactic categories of noun and verb. We investigate a graph-theoretic model of WordNet’s most important relation—synonymy—and propose measures that determine the semantic orientation of adjectives for three factors of subjective meaning. Evaluation against human judgments shows the effectiveness of the resulting measures. We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.