Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Chen, Yuxiao; Yuan, Jianbo; You, Quanzeng; Luo, Jiebo

doi:10.1145/3240508.3240533

Cited by 123 publications

(53 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Chen et al [35] (2018) explored the usage of a particular emoji in both positive and negative context. Further, their proposed attention based LSTM amalgamates this bi-sense embedding scheme to predict the sentimentalities in a better fashion, when compared to the prevailing techniques.…”

Section: Emoji and Sarcasm Analysismentioning

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WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements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

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.

show abstract

Section: Emoji and Sarcasm Analysismentioning

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WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements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

“…Other researchers have recently turned to studying sentimental analysis via machine learning-based [3], [28] methods. Combining CNN and LSTM [2], an attention mechanism [29], BERT [30] and Emoji embedding [1] are successively applied in the sentimental analysis research, greatly improving performance. Researchers also find a potential relationship between ADRs and emotional analysis in social texts [31].…”

Section: A Sentimental Analysis In Social Mediamentioning

confidence: 99%

“…More than 50 million posts are published every day according to Twitter's official reports. Therefore, Twitter provides rich large-scale multimedia data for various research opportunities [1] involving ADR detection, which focuses on automatically classifying ADRs (positive and negative) given the post content. ADR detection from social texts is an important task for discovering ADRs [2] due to the limitations of clinical experiments.…”

Section: Introductionmentioning

confidence: 99%

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Lin

Zheng

2020

IEEE Access

View full text Add to dashboard Cite

Discovering more time-effective and a wider range of adverse drug reactions (ADRs) from social texts related to feelings concerning taking medication has recently received significant interest in pharmacovigilance research. Recognizing the posts that include ADRs is an important step for detecting ADRs from social texts. The existing systems show the unsatisfactory performance due to the insufficient expression of emotions and the inadequacy of information expression in short social texts. Although these systems exploit emotional features to improve the performance of their methods, the representation of wordlevel emotional scores is insufficient for emotional expression. Moreover, most of the systems make less use of medical knowledge to enhance the detection of the potential relationship between drugs and adverse reactions in posts. Therefore, enough expression of emotion and medical knowledge in sparse medical social texts may be explored to improve system performance. This paper proposed an effective method integrating sufficient emotional expression and medical knowledge to detect ADRs from medical tweets. First, the proposed method utilized sentence-level emotional context and word-level emotional score to learn sufficient emotional information for distinguishing between ADR and non-ADR tweets. Furthermore, a co-occurrence dictionary of each drug and its relevant ADRs was constructed by means of a medical resource (MedDRA) and drug site (www.drug.com) to help the proposed model focus on posts containing drugs and ADRs. Finally, a convolutional neural network (CNN) model on the basis of bidirectional encoder representations from transformers (BERT) performed the classification task. The proposed model achieved better overall performance than the other existing methods on two Twitter datasets (F1-scores of 72.64% and 64.98% on PSB2016 and SMM4H, respectively). INDEX TERMS Adverse drug reaction, medical knowledge, emotional context, co-occurrence dictionary, social text.

show abstract

“…In simple terms, the authors emphasize on the sense of the word while creating the embedding. In the same manner, Chen et al [25] use emojis and an attention mechanism to emphasize the sense within the emojis while creating an embedding. Emoji, word, or sentiment specific embedding do not consider the variation in the sense under different topics.…”

Section: Deep Learning Sentiment Classificationmentioning

confidence: 99%

Context-Aware Sliding Window for Sentiment Classification

Masood

Abbasi

2020

IEEE Access

View full text Add to dashboard Cite

Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods. INDEX TERMS Sentiment classification, user history, temporal sliding window, deep learning, machine-learning, temporal dataset.

show abstract

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Cited by 123 publications

References 28 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

An Effective Emotional Expression and Knowledge-Enhanced Method for Detecting Adverse Drug Reactions

Context-Aware Sliding Window for Sentiment Classification

Contact Info

Product

Resources

About