Amitava Das

Bio: Amitava Das is an academic researcher from Wipro. The author has contributed to research in topics: Bengali & Sentiment analysis. The author has an hindex of 25, co-authored 115 publications receiving 2268 citations. Previous affiliations of Amitava Das include Jadavpur University & University of North Texas.

Code Mixing: A Challenge for Language Identification in the Language of Social Media

Abstract: In social media communication, multilingual speakers often switch between languages, and, in such an environment, automatic language identification becomes both a necessary and challenging task. In this paper, we describe our work in progress on the problem of automatic language identification for the language of social media. We describe a new dataset that we are in the process of creating, which contains Facebook posts and comments that exhibit code mixing between Bengali, English and Hindi. We also present some preliminary word-level language identification experiments using this dataset. Different techniques are employed, including a simple unsupervised dictionary-based approach, supervised word-level classification with and without contextual clues, and sequence labelling using Conditional Random Fields. We find that the dictionary-based approach is surpassed by supervised classification and sequence labelling, and that it is important to take contextual clues into consideration.

Fighting an Infodemic: COVID-19 Fake News Dataset.

Abstract: Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.46% F1-score with SVM. The data and code is available at: this https URL

Identifying Languages at the Word Level in Code-Mixed Indian Social Media Text

Abstract: Language identification at the document level has been considered an almost solved problem in some application areas, but language detectors fail in the social media context due to phenomena such as utterance internal code-switching, lexical borrowings, and phonetic typing; all implying that language identification in social media has to be carried out at the word level. The paper reports a study to detect language boundaries at the word level in chat message corpora in mixed EnglishBengali and English-Hindi. We introduce a code-mixing index to evaluate the level of blending in the corpora and describe the performance of a system developed to separate multiple languages.

SentiWordNet for Indian Languages

Abstract: The discipline where sentiment/ opinion/ emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found in sentiment lexicon literature deal mostly with English texts. In this article, we propose multiple computational techniques like, WordNet based, dictionary based, corpus based or generative approaches for generating SentiWordNet(s) for Indian languages. Currently, SentiWordNet(s) are being developed for three Indian languages: Bengali, Hindi and Telugu. An online intuitive game has been developed to create and validate the developed SentiWordNet(s) by involving Internet population. A number of automatic, semi-automatic and manual validations and evaluation methodologies have been adopted to measure the coverage and credibility of the developed SentiWordNet(s).

SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

Abstract: In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English)and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.

Current State of Text Sentiment Analysis from Opinion to Emotion Mining

Abstract: Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification.

Abstract: The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domain-specific data. In this paper, we propose a new evaluation framework (TweetEval) consisting of seven heterogeneous Twitter-specific classification tasks. We also provide a strong set of baselines as starting point, and compare different language modeling pre-training strategies. Our initial experiments show the effectiveness of starting off with existing pre-trained generic language models, and continue training them on Twitter corpora.

Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

Abstract: Sentiment analysis is the task of automatically determining from text the attitude, emotion, or some other affectual state of the author. This chapter summarizes the diverse landscape of tasks and applications associated with sentiment analysis. We outline key challenges stemming from the complexity and subtlety of language use, the prevalence of creative and non-standard language, and the lack of paralinguistic information, such as tone and stress markers. We describe automatic systems and datasets commonly used in sentiment analysis. We summarize several manual and automatic approaches to creating valence- and emotion-association lexicons. We also discuss preliminary approaches for sentiment composition (how smaller units of text combine to express sentiment) and approaches for detecting sentiment in figurative and metaphoric language—these are the areas where we expect to see significant work in the near future.