Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model

Phan, Huyen Trang; Tran, Cuong; Nguyen, Ngoc Thanh; Hwang, Dosam

doi:10.1109/access.2019.2963702

Cited by 105 publications

(43 citation statements)

References 27 publications

(55 reference statements)

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“…The authors used different methods, some based on a naive Bayesian classifier (to determine the field of the text in which the polysemic sentiment word is), an extended sentiment dictionary and the design of sentiment score rules. While others authors build a Feature Ensemble Model (FEM) and a Convolutional Neural Model (CNN) for tweets containing fuzzy sentiment [30].…”

Section: B Lexicon-based Techniquesmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

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Section: B Lexicon-based Techniquesmentioning

confidence: 99%

On the Statistical and Temporal Dynamics of Sentiment Analysis

et al. 2020

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“…Chouchani et al [18] used information about social influence processes to improve emotion analysis. Phan et al [19] proposed a new approach based on a feature ensemble model related to tweets containing fuzzy emotion by taking into account elements such as lexical, word-type, semantic, position, and emotion polarity of words. Chung et al [20] developed a novel framework for dissecting emotion and examining user influence in social media which comprehensively considered emotions, social positions, influence and other factors.…”

Section: Recommendation Algorithms Based On Content Descriptionmentioning

confidence: 99%

“…At present, the research of deep learning technology of integrating multi-source heterogeneous data, fusion scoring matrix and review text, and multi-featured collaborative recommendation has become a hot topic [17] [18][19][20]. Based on the above research, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion (DMHR algorithm), which aims at the balance of user score distribution and the difficulty of multi-recommendation in recommendation system.…”

Section: Introductionmentioning

confidence: 99%

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

et al. 2020

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The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

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“…Given its popularity and these numerous applications, a substantial effort by the information retrieval (IR) and natural language processing (NLP) communities has been dedicated to developing techniques, algorithms and approaches to detect affect more accurately in social media messages (Pang and Lee, 2008;Ravi and Ravi, 2015). Due to the prevalence of conducting sentiment analysis on large unstructured user generated content, accurate detection poses a challenge (Phan et al, 2020). Nevertheless, limitations on text length imposed by most social media and microblogging services such as Twitter arguably do nothing to discourage creative language use such as sarcasm and irony which allow strong sentiments to be expressed effectively (Ghosh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A qualitative analysis of sarcasm, irony and related #hashtags on Twitter

Sýkora¹,

Elayan²,

Jackson

2020

Big Data & Society

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As the use of automated social media analysis tools surges, concerns over accuracy of analytics have increased. Some tentative evidence suggests that sarcasm alone could account for as much as a 50% drop in accuracy when automatically detecting sentiment. This paper assesses and outlines the prevalence of sarcastic and ironic language within social media posts. Several past studies proposed models for automatic sarcasm and irony detection for sentiment analysis; however, these approaches result in models trained on training data of highly questionable quality, with little qualitative appreciation of the underlying data. To understand the issues and scale of the problem, we are the first to conduct and present results of a focused manual semantic annotation analysis of two datasets of Twitter messages (in total 4334 tweets), associated with; (i) hashtags commonly employed in automated sarcasm and irony detection approaches, and (ii) tweets relating to 25 distinct events, including, scandals, product releases, cultural events, accidents, terror incidents, etc. We also highlight the contextualised use of multi-word hashtags in the communication of humour, sarcasm and irony, pointing out that many sentiment analysis tools simply fail to recognise such hashtag-based expressions. Our findings also offer indicative evidence regarding the quality of training data used for automated machine learning models in sarcasm, irony and sentiment detection. Worryingly only 15% of tweets labelled as sarcastic were truly sarcastic. We highlight the need for future research studies to rethink their approach to data preparation and a more careful interpretation of sentiment analysis.

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Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model

Cited by 105 publications

References 27 publications

On the Statistical and Temporal Dynamics of Sentiment Analysis

On the Statistical and Temporal Dynamics of Sentiment Analysis

A hybrid recommendation model in social media based on deep emotion analysis and multi-source view fusion

A qualitative analysis of sarcasm, irony and related #hashtags on Twitter

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