Faculty Publications
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Item Towards sentiment orientation data set enrichment(Association for Computing Machinery acmhelp@acm.org, 2016) Sankaranarayanan, S.; Ingale, D.; Bhambhu, R.; Chandrasekaran, K.Sentiment orientation data sets referred to variously as affective word lists, opinion lexicons, sentiment lexicons, emotion lexicons or sentiment dictionaries contain a list of words scored for the degree of positive and negative emotion they exhibit. Although these lists have been used extensively for the sentiment analysis of text data, they contain a limited number of words that are often inadequate for data obtained from modern text sources dominated by the inuence of social media that has resulted in the creation and coining of new words on a regular basis. In an effort to enrich these data sets with new words, we propose two methods. The first method involves the sentiment analysis of portmanteau words. We have hypothesized that the sentiment score of a portmanteau word; which is a combination of two (or more) words and their meanings into a single new word; can be determined as a function of the sentiment scores of its component words. Regression analysis has been used to determine this functional relationship and several cases arising from the above have been evaluated on a data set constructed from SentiWordNet. The second method is an in situ approach for sentiment discovery for unknown words that uses labeled tweets and words from the sentiment orientation data set as inputs to discover the sentiment score of the unknown word. In order to validate the resultant score, we have also used a novel validation-feedback mechanism akin to crossvalidation. Both these methods produce acceptable levels of accuracy proving that they can be implemented in practice. © 2016 ACM.Item Sentiment extraction from naturalistic video(Elsevier B.V., 2018) Radhakrishnan, V.; Joseph, C.; Chandrasekaran, K.Sentiment analysis on video is quite an unexplored field of research wherein the emotion and sentiment of the speaker are extracted by processing the frames, audio and text obtained from the video. In recent times, sentiment analysis from naturalistic audio has been an upcoming field of research. This is typically done by performing automatic speech recognition on audio, followed by extracting the sentiment exhibited by the speaker. On the other hand, techniques for extracting sentiments from text are quite developed and tech giants have already optimized these methods to process large amounts of customer review, feedback and reactions. In this paper, a new model for sentiment analysis from audio is proposed which is a hybrid of Keyword Spotting System (KWS) and Maximum Entropy (ME) Classifier System. This model is developed with the aim to outperform other conventional classifiers and to provide a single integrated system for audio and text processing. In addition, a web application for dynamic processing of YouTube videos is described. The WebApp provides an index-based result for each phrase that is detected in the video. Often, the emotion of the viewer of a video corresponds to its content. In this regard, it is useful to map these emotions to the text transcript of the video and assign a suitable weight to it while predicting the sentiment that the speaker exhibits. This paper describes such an application that was developed to analyze facial expressions using Affdex API. Thus, using the combined statistics from all the three aforementioned components, a robust and portable system for emotion detection is obtained that provides accurate predictions and can be deployed on any modern systems with minimal configuration changes. © 2018 The Authors. Published by Elsevier B.V.