Sentiment Analysis CSAM Model to Discover Pertinent Conversations in Twitter Microblogs

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Imen Fadhli 1,* Lobna Hlaoua 2 Mohamed Nazih Omri 2

1. MARS Research Laboratory LR17ES05, University of Sousse, ISITCom, 4011 Hammam Sousse, Tunisia

2. MARS Research Laboratory LR17ES05, University of Sousse, Tunisia

* Corresponding author.


Received: 21 Mar. 2022 / Revised: 25 Jun. 2022 / Accepted: 19 Aug. 2022 / Published: 8 Oct. 2022

Index Terms

Conversational, sentiment analysis, word embedding, belief function, conditional probability


In recent years, the most exploited sources of information such as Facebook, Instagram, LinkedIn and Twitter have been considered to be the main sources of misinformation. The presence of false information in these social networks has a very negative impact on the opinions and the way of thinking of Internet users. To solve this problem of misinformation, several techniques have been used and the most popular is the sentiment analysis. This technique, which consists in exploring opinions on corpora of texts, has become an essential topic in this field. In this article, we propose a new approach, called Conversational Sentiment Analysis Model (CSAM), allowing, from a text written on a subject through messages exchanged between different users, called a conversation, to find the passages describing feelings, emotions, opinions and attitudes. This approach is based on: (i) the conditional probability in order to analyse sentiments of different conversation items in Twitter microblog, which are characterized by small sizes, the presence of emoticons and emojis, (ii) the aggregation of conversation items using the uncertainty theory to evaluate the general sentiment of conversation. We conducted a series of experiments based on the standard Semeval2019 datasets, using three standard and different packages, namely a library for sentiment analysis TextBlob, a dictionary, a sentiment reasoner Flair and an integration-based framework for the Vader NLP task. We evaluated our model with two dataset SemEval 2019 and ScenarioSA, the analysis of the results, which we obtained at the end of this experimental study, confirms the feasibility of our model as well as its performance in terms of precision, recall and F-measurement.

Cite This Paper

Imen Fadhli, Lobna Hlaoua, Mohamed Nazih Omri, "Sentiment Analysis CSAM Model to Discover Pertinent Conversations in Twitter Microblogs", International Journal of Computer Network and Information Security(IJCNIS), Vol.14, No.5, pp.28-46, 2022. DOI:10.5815/ijcnis.2022.05.03


[1]Pang et al. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2:1– 135, 01 2008.
[2]Zhang et al. Scenariosa: a dyadic conversational database for interactive sentiment analysis. IEEE Access, 8:90652–90664, 2020.
[3]Winata et al. Caire_hkust at semeval-2019 task 3: Hierarchical attention for dialogue emotion classification. arXiv preprint arXiv:1906.04041, 2019.
[4]Wei et al. Modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity. arXiv preprint arXiv:1909.08211, 2019.
[5]Mohamed Nazih Omri. Possibilistic pertinence feedback and semantic networks for goal’s extraction. Asian Journal of Information Technology, 3(4):258– 265, 2004.
[6]Mohamed Nazih Omri. Relevance feedback for goal’s extraction from fuzzy semantic networks. Asian Journal of Information Technology, 3(6):434–440, 2004.
[7]Bing Liu. Opinion mining and sentiment analysis. In Web Data Mining, pages 459–526. Springer, 2011.
[8]Sailunaz et al. Emotion and sentiment analysis from twitter text. Journal of Computational Science, 36:101003, 2019.
[9]Lian et al. Smin: Semi-supervised multi-modal interaction network for conversational emotion recognition. IEEE Transactions on Affective Computing, 2022.
[10]Park et al. Plusemo2vec at semeval-2018 task 1: Exploiting emotion knowledge from emoji and# hashtags. arXiv preprint arXiv:1804.08280, 2018.
[11]Barbosa et al. Robust sentiment detection on twitter from biased and noisy data. In Coling 2010: Posters, pages 36–44, 2010.
[12]Song et al. Sacpc: A framework based on probabilistic linguistic terms for short text sentiment analysis. Knowledge-Based Systems, 194:105572, 2020.
[13]Görmez et al. Fbsem: a novel feature-based stacked ensemble method for sentiment analysis. International Journal of Information Technology and Computer Science, 6:11–22, 2020.
[14]Azer et al. Credibility detection on twitter news using machine learning approach. International Journal of Intelligent Systems and Applications, 13(3):1–10, 2021.
[15]Shobana et al. Twitter sentimental analysis. International Journal of Recent Technology and Engineering (IJRTE), 7, 2018.
[16]Pang et al. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 79–86. Association for Computational Linguistics, 2002.
[17]Das et al. Yahoo! for amazon: Extracting market sentiment from stock message boards. In Proceedings of the Asia Pacific finance association annual conference (APFA), volume 35, page 43. Bangkok, Thailand, 2001.
[18]Zhang et al. Combining lexicon-based and learningbased methods for twitter sentiment analysis. HP Laboratories, Technical Report HPL-2011, 89, 2011.
[19]Kaur et al. Twitter sentiment analysis of the indian union budget 2020. 2020.
[20]Kunal et al. Textual dissection of live twitter reviews using naive bayes. Procedia computer science, 132:307–313, 2018.
[21]Golam Mostafa, Ikhtiar Ahmed, Masum Shah Junayed, "Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data", International Journal of Information Technology and Computer Science, Vol.13, No.2, pp.38-48, 2021.
[22]Alvarez-Melis et al. Topic modeling in twitter: Aggregating tweets by conversations. In Proceedings of the International AAAI Conference on Web and Social Media, volume 10, 2016.
[23]Naouar et al. Information retrieval model using uncertain confidence’s network. International Journal of Information Retrieval Research, 7(2):33–49, 2017.
[24]Boughammoura et al. Information retrieval from deep web based on visual query interpretation. International Journal of Information Retrieval Research, 2(4):45–59, 2012.
[25]Amer et al. Recherche de conversations dans les réseaux sociaux: modélisation et expérimentations sur twitter. 2015.
[26]Meester et al. A new look at conditional probability with belief functions. Statistica Neerlandica, 73(2):274–291, 2019.
[27]Hall et al. Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association, 99(468):1015–1026, 2004.
[28]Van Campenhout et al. Maximum entropy and conditional probability. IEEE Transactions on Information Theory, 27(4):483–489, 1981.
[29]Glenn Shafer. A mathematical theory of evidence, volume 42. Princeton university press, 1976.
[30]Silva et al. Predicting misinformation and engagement in covid-19 twitter discourse in the first months of the outbreak. arXiv preprint arXiv:2012.02164, 2020.
[31]Loria et al. Textblob: simplified text processing. Secondary TextBlob: simplified text processing, 3, 2014.
[32]Hutto et al. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, volume 8, 2014.
[33]Akbik et al. Contextual string embeddings for sequence labeling. In Proceedings of the 27th international conference on computational linguistics, pages 1638–1649, 2018.
[34]Felbo et al. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524, 2017.
[35]Jianqiang et al. Deep convolution neural networks for twitter sentiment analysis. IEEE Access, 6:23253– 23260, 2018. [36] Pak et al. Twitter as a corpus for sentiment analysis and opinion mining. In LREc, volume 10, pages 1320– 1326, 2010.
[36]Carvalho et al. On the evaluation and combination of state-of-the-art features in twitter sentiment analysis. Artificial Intelligence Review, pages 1–50, 2020.
[37]Carvalho et al. An assessment study of features and meta-level features in twitter sentiment analysis. In Proceedings of the Twenty-second European Conference on Artificial Intelligence, pages 769–777, 2016.
[38]Agarwal et al. Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media (LSM 2011), pages 30–38, 2011.
[39]Heck et al. Deep learning of knowledge graph embeddings for semantic parsing of twitter dialogs. In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pages 597–601. IEEE, 2014.
[40]Philippe Smets. The combination of evidence in the transferable belief model. IEEE Transactions on pattern analysis and machine intelligence, 12(5):447–458, 1990.
[41]Zhang et al. Scenariosa: a dyadic conversational database for interactive sentiment analysis. IEEE Access, 8:90652–90664, 2020.
[42]Chatterjee et al. Semeval-2019 task 3: Emocontext contextual emotion detection in text. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 39–48, 2019a.
[43]Alswaidan et al. A survey of state-of-the-art approaches for emotion recognition in text. Knowledge and Information Systems, pages 1–51, 2020.
[44]Ke et al. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30:3146–3154, 2017.
[45]Devlin et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[46]Peters et al. Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.
[47]Chatterjee et al. Understanding emotions in text using deep learning and big data. Computers in Human Behavior, 93:309–317, 2019.