Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

Abhishek, Kumar; Ekbal, Asif; Kawahra, Daisuke; Kurohashi, Sadao

doi:10.1109/ijcnn.2019.8852352

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…Abhishek Kumar et al [18](2019) incorporated a bidirectional LSTM which studies the context of a word in a sentence and ultimately identifies the sentence polarity. This two level approach first studied the word meanings through Distributional Thesaurus and ultimately applied it to the sentence to detect opinions and emotions.…”

Section: Sentiment Analysis and Prediction Of Chosen Politicalmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

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Twitter is one of the most popular social networking platforms of today’s generation and is a fundamental tool in harvesting the data of many users worldwide. Many discussions ranging from current affairs, news sharing, filing complaints to advertising and discussing some common interests etc. happen on Twitter. It is widely used by many famous politicians as a prominent communication medium to address large masses ,owing to its mass usage and popularity . Thus, it is safe to assume that people communicate their political ideologies on Twitter. Many works have been done so far to deduce the stance of user towards a particular party by performing sentiment analysis on their tweets using popular classifiers. To find connected and similar users, earlier works generated a social network graph, based on the assumption that friends and followers share similar interests, which might not be true in all cases. In contrast, the proposed work employs the concept of ensemble classifier (a single classifier generated from several base learning classifiers) to analyze the tweets and makes use of multiple interaction elements like followers/ following, mentions, re-tweets ,hash tags etc. to infer which political party a user identifies with. These interaction elements project out the homophily (users who share same beliefs and choices) amongst the users. The proposed study can be encompassed to any domain and can be used by advertising agencies, marketing companies, e-commerce, heath care etc. to identify their target audience.

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Section: Sentiment Analysis and Prediction Of Chosen Politicalmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In , the authors proposed a RNN framework capable of learning inter-modal interaction among the different modalities using the auto-encoder mechanism. As emotion and sentiment are two very closely related tasks, in recent time there is a trend on modeling both sentiment and emotion of an utterance simultaneously (Akhtar et al, 2019a;Akhtar et al, 2019b;Kumar et al, 2019;Akhtar et al, 2020). In (Akhtar et al, 2020), the authors employed the concept of multi-task learning for multi-modal affect analysis and explored a contextual inter-modal attention framework that aimed in leveraging the association among the neighboring utterances and their multi-modal information.…”

Section: Related Workmentioning

confidence: 99%

MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations

Firdaus¹,

Chauhan²,

Ekbal³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Emotion and sentiment classification in dialogues is a challenging task that has gained popularity in recent times. Humans tend to have multiple emotions with varying intensities while expressing their thoughts and feelings. Emotions in an utterance of dialogue can either be independent or dependent on the previous utterances, making the task complex and interesting. Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting. On the other hand, sentiment analysis in dialogue or conversation helps in understanding the perspective of the user with respect to the ongoing conversation. Besides text, additional information in the form of audio and video assists in identifying the correct emotions with the appropriate intensity and sentiments in an utterance of a dialogue. Lately, quite a few datasets have been made available for emotion and sentiment classification in dialogues. Still, these datasets are imbalanced in representing different emotions and consist of only a single emotion. Hence, we present at first a large-scale balanced Multimodal Multi-label Emotion, Intensity, and Sentiment Dialogue dataset (MEISD) collected from different TV series that has textual, audio, and visual features, and then establish a baseline setup for further research.

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“…Emotion analysis follows the same procedures as sentiment analysis, but emotion analysis has a different classification goal. Identifying sentiments and emotions from text are treated as separate problems, although sentiments can be identified from the emotions [61].…”

Section: ) Sentiment and Emotion Analysismentioning

confidence: 99%

Palaute: An Online Text Mining Tool for Analyzing Written Student Course Feedback

et al. 2021

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Collecting student feedback is commonplace in universities. These surveys usually include both openended questions and Likert-type scale questions but the answers to open questions tend not to be analysed further than simply reading them. Recent research has shown that text mining and machine learning methods can be utilized to extract useful topics from masses of open student feedback. However, to our knowledge, not many off-the-shelf applications exist for processing open-ended student feedback automatically. Additionally, the use of text mining tools may not be available to all educators, as they require in-depth knowledge of text-mining, data-analysis, or programming tools. To address this gap the current study presents a tool (Palaute) for analyzing written student feedback using topic modeling and emotion analysis. The utility of this tool is demonstrated with two real life use cases: First we analyze student feedback data collected from courses in a software engineering degree programme, and then feedback from all courses organized in a university. In our experiments, the analysis of open-ended feedback revealed that on certain software engineering course modules the workload is perceived as heavy, and on some programming courses the automatic code grader could be improved. The university-wide analysis produced indicators of good teaching quality, such as interesting courses, but also some concrete improvement points like the time given to complete course assignments. Therefore, the use of the tool resulted in actionable improvement points, which could not have been identified using only numeric feedback metrics. Based on the demonstrated utility, this paper describes the design and implementation of our open-source tool.INDEX TERMS curriculum development, educational technology, student evaluation of teaching, text processing

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Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis

Cited by 14 publications

References 38 publications

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations

Palaute: An Online Text Mining Tool for Analyzing Written Student Course Feedback

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