Santosh Kumar Bharti

Parsing-based Sarcasm Sentiment Recognition in Twitter Data

Sentiment Analysis is a technique to identify peoples opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say (especially in order to insult or wit someone, to show irritation, or to be funny). People often expressed it verbally through the use of heavy tonal stress and certain gestural clues like rolling of the eyes. These tonal and gestural clues are obviously not available for expressing sarcasm in text, making its detection reliant upon other factors. In this paper, two approaches to detect sarcasm in the text of Twitter data were proposed. The first is a parsing-based lexicon generation algorithm (PBLGA) and the second was to detect sarcasm based on the occurrence of the interjection word. The combination of two approaches is also shown and compared with the existing state-of-the-art approach to detect sarcasm. First approach attains a 0.89, 0.81 and 0.84 precision, recall and f - score respectively. Second approach attains 0.85, 0.96 and 0.90 precision, recall and f - score respectively in tweets with sarcastic hashtag.

Harnessing Online News for Sarcasm Detection in Hindi Tweets

Detection of sarcasm in Indian languages is one of the most challenging tasks of Natural Language Processing (NLP) because Indian languages are ambiguous in nature and rich in morphology. Though Hindi is the fourth popular language in the world, sarcasm detection in it remains unexplored. One of the reasons is the lack of annotated resources. In the absence of sufficient resources, processing the NLP tasks such as POS tagging, sentiment analysis, text mining, sarcasm detection, etc., becomes tough for researchers. Here, we proposed a framework for sarcasm detection in Hindi tweets using online news. In this article, the online news is considered as the context of a given tweet during the detection of sarcasm. The proposed framework attains an accuracy of 79.4%.

Sarcasm Analysis on Twitter Data Using Machine Learning Approaches

Sarcasm analysis, being one of the toughest challenges in natural language processing (NLP), has become a hot topic of research these days. A lot of work has already been done in the field of sentiment analysis, but there are huge challenges still being faced in identification of sarcasm. The property of sarcasm that makes it difficult to analyze and detect is the gap between its literal and intended meaning. Detecting sentiment in social media like Facebook, Twitter, online blogs, and reviews has become an essential task as they influence every business organization. In this chapter, four approaches were proposed, namely parsing-based lexical generation algorithm, likes and dislikes contradiction, tweet contradicting universal facts, and tweet contradicting temporary facts. The aim of the proposed methods is to extract text features such as lexical, hyperbole, behavioral, and universal facts. Further, four machine learning classifiers, namely support vector machine, Naive Bayes, maximum entropy, and decision tree, were deployed. Finally, we trained these classifiers using an extracted feature set to identify sarcasm in Twitter data. This work attains a considerable accuracy improvement over existing techniques.

Text Summarization with Automatic Keyword Extraction in Telugu e-Newspapers

Summarization is the process of shortening a text document to make a summary that keeps the main points of the actual document. Extractive summarizers work on the given text to extract sentences that best express the message hidden in the text. Most extractive summarization techniques revolve around the concept of finding keywords and extracting sentences that have more keywords than the rest. Keyword extraction usually is done by extracting relevant words having a higher frequency than others, with stress on important one’s. Manual extraction or annotation of keywords is a tedious process brimming with errors involving lots of manual effort and time. In this work, we proposed an algorithm that automatically extracts keyword for text summarization in Telugu e-newspaper datasets. The proposed method compares with the experimental result of articles having the similar title in five different Telugu e-newspapers to check the similarity and consistency in summarized results.