Sentiment strength detection for the social web

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543

Content Analysis: An Introduction to its Methodology.

Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,346

Data Mining

Witten, I. H. (2008) · Cited by 9,143

Opinion Mining and Sentiment Analysis

Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957

Opinion mining and sentiment analysis

Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372

Advances in kernel methods: support vector learning

Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809

Making Large-Scale SVM Learning PracticalPreprint

Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,517

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

Peter, P., & Turney (2002) · Cited by 3,646

Lexicon-based methods for sentiment analysis

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Pak, A., & Paroubek, P. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/47akcj8foo4d · Cited by 2,784

Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams

Gupta, V., & Hewett, R. (2020) · Information · doi:10.3390/info11070341 · Cited by 23

Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis

Wijayanti, R., & Arisal, A. (2021) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3425632 · Cited by 23

Spatial Pyramid Pooling Mechanism in 3D Convolutional Network for Sentence-Level Classification

Ouyang, X., Gu, K., & Zhou, P. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2852502 · Cited by 22

Multi-Class Sentiment Analysis Comparison Using Support Vector Machine (SVM) and BAGGING Technique-An Ensemble Method

Sharma, S., Srivastava, S., Kumar, A., & Dangi, A. (2018) · International Conference on Smart Computing and Electronic Enterprise (ICSCEE) · doi:10.1109/icscee.2018.8538397 · Cited by 22

Predicting Community Smells’ Occurrence on Individual Developers by Sentiments

Huang, Z., Shao, Z., Fan, G., Gao, J., Zhou, Z., Yang, K., & Yang, X. (2021) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc52881.2021.00030 · Cited by 22

Data Analytics on Online Student Engagement Data for Academic Performance Modeling

Tao, X., Shannon-Honson, A., Delaney, P., Li, L., Dann, C. E., Li, Y., & Xie, H. (2022) · IEEE Access · doi:10.1109/access.2022.3208953 · Cited by 22

A survey on pragmatic processing techniques

Mao, R., Ge, M., Han, S., Wei, L., He, K., Zhu, L., & Cambria, E. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102712 · Cited by 24

Domain-Based Lexicon Enhancement for Sentiment Analysis.

Muhammad, A., Wiratunga, N., Lothian, R., & Glassey, R. J. (2013) · Cited by 22

Dystemo

Sintsova, V., & Pu, P. (2016) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2912147 · Cited by 22

Twitter Analysis for Intelligent Transportation

Alhumoud, S. O. (2018) · The Computer Journal · doi:10.1093/comjnl/bxy129 · Cited by 22

Enhance sentiment analysis on social networks with social influence analytics

Chouchani, N., & Abed, M. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01234-0 · Cited by 22

Investigating the complete corpus of referendum and elections tweets

Antonakaki, D., Spiliotopoulos, D., Samaras, C. V., Ioannidis, S., & Fragopoulou, P. (2016) · Advances in Social Networks Analysis and Mining · doi:10.5555/3192424.3192443 · Cited by 21

Microblogs data management: a survey

Magdy, A., Abdelhafeez, L., Kang, Y., Ong, E., & Mokbel, M. F. (2019) · The VLDB Journal · doi:10.1007/s00778-019-00569-6 · Cited by 21

We Are Family: Analyzing Communication in GitHub Software Repositories and Their Forks

Brisson, S., Noei, E., & Lyons, K. A. (2020) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner48275.2020.9054834 · Cited by 21

A Combination of Machine Learning and Lexicon Based Techniques for Sentiment Analysis

Neshan, S. A. S., & Akbari, R. (2020) · International Conference on Web Research (ICWR) · doi:10.1109/icwr49608.2020.9122298 · Cited by 21

Socio-Technical Work-Rate Increase Associates With Changes in Work Patterns in Online Projects

Sarker, F., Vasilescu, B., Blincoe, K., & Filkov, V. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00099 · Cited by 21

A Joint Segmentation and Classification Framework for Sentiment Analysis

Tang, D., Wei, F., Qin, B., Dong, L., Liu, T., & Zhou, M. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1054 · Cited by 20

FinnSentiment: a Finnish social media corpus for sentiment polarity annotation

Lindén, K., Jauhiainen, T., & Hardwick, S. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09644-5 · Cited by 20

Emoji-SP, the Spanish emoji database: Visual complexity, familiarity, frequency of use, clarity, and emotional valence and arousal norms for 1031 emojis

Ferré, P., Haro, J., Sánchez, M. Á. P., Moreno, I., & Hinojosa, J. A. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01893-6 · Cited by 20

A lexicon-based approach for sentiment analysis of multimodal content in tweets

Thangavel, P., & Lourdusamy, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14411-3 · Cited by 20

Sentiment Lexicon Interpolation and Polarity Estimation of Objective and Out-Of-Vocabulary Words to Improve Sentiment Classification on Microblogging

Kaewpitakkun, Y., Shirai, K., Shirai, K., 51700, 30302970, Mohd, M., & 51701 (2014) · Institutional Repositories DataBase (IRDB) · Cited by 20

Text Classification Models for Web Content Filtering and Online Safety

Liu, S., & Forss, T. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.143 · Cited by 20

On the feasibility of predicting popular news at cold start

Arapakis, I., Cambazoglu, B. B., & Lalmas, M. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23756 · Cited by 20

Sentiment lexicon adaptation with context and semantics for the social web

Saif, H., Fernández, M., Kastler, L., & Alani, H. (2017) · Semantic Web · doi:10.3233/sw-170265 · Cited by 20

Combing Semantic and Prior Polarity Features for Boosting Twitter Sentiment Analysis Using Ensemble Learning

Zhao, J. (2016) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2016.124 · Cited by 20

Reliability and Perceived Value of Sentiment Analysis for Twitter Data

Jussila, J., Vuori, V., Okkonen, J., & Helander, N. (2017) · Springer proceedings in business and economics · doi:10.1007/978-3-319-56288-9_7 · Cited by 20

Parsing Based Sarcasm Detection from Literal Language in Tweets

Basha, S. M., & Rajput, D. S. (2018) · Recent Advances in Computer Science and Communications · doi:10.2174/2213275911666180531112306 · Cited by 20

A very public cull – The anatomy of an online issue public

Sandover, R., Kinsley, S., & Hinchliffe, S. (2018) · Geoforum · doi:10.1016/j.geoforum.2018.08.016 · Cited by 20

SmartPI: Understanding Permission Implications of Android Apps from User Reviews

Wang, R., Wang, Z., Tang, B., Zhao, L., & Wang, L. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2934441 · Cited by 22

A systematic study on the role of SentiWordNet in opinion mining

Husnain, M., Missen, M. M. S., Akhtar, N., Coustaty, M., Mumtaz, S., & Prasath, V. B. S. (2021) · Frontiers of Computer Science · doi:10.1007/s11704-019-9094-0 · Cited by 23

Directional prediction of stock prices using breaking news on Twitter

Alostad, H., & Davulcu, H. (2017) · Web Intelligence · doi:10.3233/web-170349 · Cited by 20

Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

Chandio, B. A., Shaikh, A., Bakhtyar, M., Alrizq, M., Baber, J., Sulaiman, A. A., Rajab, A. D., & Noor, W. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.019535 · Cited by 19

Investigating the complete corpus of referendum and elections tweets

Antonakaki, D., Spiliotopoulos, D., Samaras, C. V., Ioannidis, S., & Fragopoulou, P. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752220 · Cited by 19

Competing for Temporary Advantage in a Hypercompetitive Mobile App Market

James, T. L., Qiao, Z., Shen, W., Wang, G. A., & Fan, W. (2023) · MIS Quarterly · doi:10.25300/misq/2022/15079 · Cited by 19

Recognizing emotions in restaurant online reviews: a hybrid model integrating deep learning and a sentiment lexicon

Liu, J., Hu, S., Mehraliyev, F., Zhou, H., Yu, Y., & Yang, L. (2023) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-02-2023-0244 · Cited by 19

A FRAMEWORK FOR MASSIVE TWITTER DATA EXTRACTION AND ANALYSIS

Cuesta, Á., Barrero, D. F., & R-Moreno, M. D. (2014) · Malaysian Journal of Computer Science · Cited by 19

Application of Social Media for Consumer Research

Vidal, L., Ares, G., & Jaeger, S. R. (2018) · Elsevier eBooks · doi:10.1016/b978-0-08-102089-0.00006-6 · Cited by 19

Classifying facts and opinions in Twitter messages: a deep learning-based approach

Chatterjee, S., Deng, S., Liu, J., Shan, R., & Wu, J. (2018) · Journal of Business Analytics · doi:10.1080/2573234x.2018.1506687 · Cited by 19

Accurate, Fast, But Not Always Cheap: Evaluating “Crowdcoding” as an Alternative Approach to Analyze Social Media Data

Гуо, Л., Mays, K. K., Lai, S., Jalal, M., Ishwar, P., & Betke, M. (2019) · Journalism & Mass Communication Quarterly · doi:10.1177/1077699019891437 · Cited by 22

Sentiment analysis in Turkish: Supervised, semi-supervised, and unsupervised techniques

Aydın, C. R., & Güngör, T. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000200 · Cited by 19

Subjectivity Detection for Sentiment Analysis on Twitter Data

Sindhu, C., Sasmal, B., Gupta, R., & Prathipa, J. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-5329-5_43 · Cited by 19

Twitter Ontology-Driven Sentiment Analysis

Cotfas, L., Delcea, C., Roxin, I., & Paun, R. (2015) · Studies in computational intelligence · doi:10.1007/978-3-319-16211-9_14 · Cited by 19

Temporal analysis of radical dark web forum users

Park, A. J., Beck, B., Fletche, D., Lam, P., & Tsang, H. H. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752341 · Cited by 19

Adjusting Chatbot Conversation to User Personality and Mood

Galitsky, B. A. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-61641-0_3 · Cited by 19

Rule Based Fuzzy Computing Approach on Self-Supervised Sentiment Polarity Classification with Word Sense Disambiguation in Machine Translation for Hindi Language

Chauhan, S., Shet, J. P., Beram, S. M., Jagota, V., Dighriri, M. A., Ahmad, M. W., Hossain, M. S., & Rizwan, A. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3574130 · Cited by 18

The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

Molenaar, A., Jenkins, E. L., Brennan, L. L., Lukose, D., & McCaffrey, T. A. (2023) · Nutrition Research Reviews · doi:10.1017/s0954422423000069 · Cited by 20

Enhancing Accuracy in Social Media Sentiment Analysis through Comparative Studies using Machine Learning Techniques

Yogi, K. S., Gowda, D., V, Sindhu, D., Soni, H., Mukherjee, S., & Madhu, G. C. (2024) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs61492.2024.10616441 · Cited by 18

Studying Animation for Real-Time Visual Analytics: A Design Study of Social Media Analytics in Emergency Management

Calderón, N. A., Arias‐Hernández, R., & Fisher, B. (2014) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2014.176 · Cited by 18

Identifying the influential bloggers: a modular approach based on sentiment analysis

Ishfaq, U., Khan, H. U., & Iqbal, K. (2017) · Journal of Web Engineering · Cited by 18

#europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study)

Coşkun, M., & Özturan, M. (2018) · Information · doi:10.3390/info9050102 · Cited by 18

How Angry are Your Customers? Sentiment Analysis of Support Tickets that Escalate

Werner, C., Tapuc, G., Montgomery, L., Sharma, D., Dodos, S., & Damian, D. (2018) · doi:10.1109/affectre.2018.00006 · Cited by 18

Reason and passion in public discussion on Sina Weibo

Liu, X., Xu, W. W., & Li, B. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2019.101285 · Cited by 18

Do Machines Replicate Humans? Toward a Unified Understanding of Radicalizing Content on the Open Social Web

Hall, M., Logan, M. K., Ligon, G. S., & Derrick, D. C. (2019) · Policy & Internet · doi:10.1002/poi3.223 · Cited by 18

Economic News

Vliegenthart, R., Damstra, A., Boukes, M., & Jonkman, J. G. F. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108950916 · Cited by 18

Measuring the Impact of Virtual Communities on the Intention to Use Telemedicine Services

Priescu, I., & Oncioiu, I. (2022) · Healthcare · doi:10.3390/healthcare10091685 · Cited by 18

Using Affect as a Communication Modality to Improve Human-Robot Communication in Robot-Assisted Search and Rescue Scenarios

Akgun, S. A., Ghafurian, M., Crowley, M., & Dautenhahn, K. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3221922 · Cited by 18

What Goes Around Comes Around: Learning Sentiments in Online Medical Forums

Bobicev, V., Sokolova, M., & Oakes, M. (2015) · Cognitive Computation · doi:10.1007/s12559-015-9327-y · Cited by 19

EMTk - The Emotion Mining Toolkit

Calefato, F., Lanubile, F., Novielli, N., & Quaranta, L. (2019) · IEEE/ACM International Workshop on Emotion Awareness in Software Engineering (SEmotion) · doi:10.1109/semotion.2019.00014 · Cited by 18

On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions

Deveaud, R., Albakour, M., Macdonald, C., & Ounis, I. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2661956 · Cited by 20

Multi-level out-of-vocabulary words handling approach

Lochter, J. V., Silva, R. M., & de Almeida, T. D. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108911 · Cited by 17

Improving the detection of community smells through socio‐technical and sentiment analysis

Almarimi, N., Ouni, A., Chouchen, M., & Mkaouer, M. W. (2022) · Journal of Software Evolution and Process · doi:10.1002/smr.2505 · Cited by 17

Sentiment Analysis Tools for Movie Review Evaluation - A Survey

Tetteh, M., & Thushara, M. G. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142834 · Cited by 17

Mining and characterizing hybrid apps

Ali, M., & Mesbah, A. (2016) · doi:10.1145/2993259.2993263 · Cited by 17

Combining a rule-based classifier with weakly supervised learning for twitter sentiment analysis

Siddiqua, U. A., Ahsan, T., & Chy, A. N. (2016) · International Conference on Innovations in Science, Engineering and Technology (ICISET) · doi:10.1109/iciset.2016.7856499 · Cited by 17

Understanding public opinion to the introduction of minimum unit pricing in Scotland: a qualitative study using Twitter

Wright, L. A., Golder, S., Balkham, A., & McCambridge, J. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-029690 · Cited by 17

Estimate Sentiment of Crowds from Social Media during City Events

Gong, V. X., Daamen, W., Bozzon, A., & Hoogendoorn, S. P. (2019) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198119846461 · Cited by 17

Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches

Perea‐Ortega, J. M., Valdivia, M. T. M., López, L. A. U., & Martínez‐Cámara, E. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22884 · Cited by 17

Differential Emotions and the Stock Market - The Case of Company-specific Trading

Risius, M., Akolk, F., & Beck, R. (2015) · IT University Of Copenhagen (IT University of Copenhagen) · doi:10.18151/7217449 · Cited by 17

A Review on Negation Role in Twitter Sentiment Analysis

Gupta, I., & Joshi, N. (2021) · International Journal of Healthcare Information Systems and Informatics · doi:10.4018/ijhisi.20211001.oa14 · Cited by 16

Methods for aggregating investor sentiment from social media

Liu, Q., & Son, H. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03434-2 · Cited by 16

Emotions, Demographics and Sociability in Twitter Interactions

Lerman, K., Arora, M., Gallegos, L., Kumaraguru, P., & García, D. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v10i1.14728 · Cited by 16

A review on political analysis and social media

Alonso, M. Á., & Vilares, D. (2016) · RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante) · Cited by 16

An Ensemble Based Method for Predicting Emotion Intensity of Tweets

Madisetty, S., & Desarkar, M. S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71928-3_34 · Cited by 16

Two simple and effective ensemble classifiers for Twitter sentiment analysis

Yan, Y., Yang, H. Q., & Wang, H. (2017) · 2017 Computing Conference · doi:10.1109/sai.2017.8252275 · Cited by 16

Contextual semantics using hierarchical attention network for sentiment classification in social internet-of-things

Kumar, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11262-8 · Cited by 16

Sentiment analysis researches story narrated by topic modeling approach

Rouhani, S., & Mozaffari, F. (2022) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2022.100309 · Cited by 16

Sentiment analysis using lexico-semantic features

Mohd, M., Javeed, S., Nowsheena, Wani, M. A., & Khanday, H. A. (2022) · Journal of Information Science · doi:10.1177/01655515221124016 · Cited by 16

Classification of Arabic Twitter Users: A Study Based on User Behaviour and Interests

Alabdullatif, A., Shahzad, B., & Alwagait, E. (2016) · Mobile Information Systems · doi:10.1155/2016/8315281 · Cited by 16

Exploring fine-grained sentiment values in online product reviews

Teh, P. L., Pak, I., Rayson, P., & Piao, S. S. (2015) · IEEE Conference on Open Systems · doi:10.1109/icos.2015.7377288 · Cited by 16

Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction

Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2807819 · Cited by 16

Improving sentiment scoring mechanism: a case study on airline services

Kaur, W., & Balakrishnan, V. (2018) · Industrial Management & Data Systems · doi:10.1108/imds-07-2017-0300 · Cited by 16

Reverse Engineering the Behaviour of Twitter Bots

Bello, B. S., Heckel, R., & Minku, L. L. (2018) · International Conference on Social Networks Analysis, Management and Security · doi:10.1109/snams.2018.8554675 · Cited by 10

A First Look at Emoji Usage on GitHub: An Empirical StudyPreprint

Lü, X., Cao, Y., Chen, Z., & Liu, X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.04863 · Cited by 16

Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language

Yazdani, A., Shamloo, M., Khaki, M., & Nahvijou, A. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02358-2 · Cited by 15

Less is more: Pruning BERTweet architecture in Twitter sentiment analysis

Moura, R., Carvalho, J., Plastino, A., & Paes, A. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103688 · Cited by 15

News source bias and sentiment on social media

Knutson, B. D., Hsu, T. W., Ko, M., & Tsai, J. L. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305148 · Cited by 18

Deep Tweets: from Entity Linking to Sentiment Analysis

Basile, P., Basile, V., Nissim, M., & Novielli, N. (2015) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.1308 · Cited by 15

Using language models to improve opinion detection

Belbachir, F., & Boughanem, M. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.07.001 · Cited by 15

A survey on sentiment classification algorithms, challenges and applications

Rana, M. R. R., Nawaz, A., & Iqbal, J. (2018) · Acta Universitatis Sapientiae Informatica · doi:10.2478/ausi-2018-0004 · Cited by 15

A framework for ensuring zero defects and sustainable operations in major Indian ports

Sinha, D., & Chowdhury, S. R. (2020) · International Journal of Quality & Reliability Management · doi:10.1108/ijqrm-02-2019-0062 · Cited by 15

Semantic Analysis of Sentiments through Web-Mined Twitter Corpus.

Chandra, S., Gourisaria, M. K., Harshvardhan, G. M., Rautaray, S. S., Pandey, M., & Mohanty, S. N. (2021) · Cited by 15

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.i1107.0789s419 · Cited by 15

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

Cui, S., Han, Y., Duan, Y., Li, Y., Zhu, S., & Song, C. (2023) · Entropy · doi:10.3390/e25040555 · Cited by 15

Semantic Web-Based Social Media Analysis

Cotfas, L., Delcea, C., Segault, A., & Roxin, I. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-49619-0_8 · Cited by 15

Social intelligence framework: Extracting and analyzing opinions for social CRM

Ennaji, F. Z., Fazziki, A. E., Sadgal, M., & Benslimane, D. (2015) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2015.7507229 · Cited by 15

Automatic event detection in microblogs using incremental machine learning

Bandaragoda, T., Silva, D. D., & Alahakoon, D. (2017) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23896 · Cited by 15

YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model

He, Y., Yu, L.-C., Lai, K.-H. R., & Liu, W. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5233 · Cited by 15

Combining Semantic and Prior Polarity for Boosting Twitter Sentiment Analysis

Jianqiang, Z., & Cao, X. (2015) · IEEE International Conference on Smart City/SocialCom/SustainCom · doi:10.1109/smartcity.2015.171 · Cited by 15

Exploring Deep Recurrent Convolution Neural Networks for Subjectivity Classification

Zhang, X., Huang, S., Zhao, J., Du, X., & He, F. (2018) · IEEE Access · doi:10.1109/access.2018.2885362 · Cited by 15

Emotion and sentiment in social and expressive media: Introduction to the special issue

Rosso, P., Bosco, C., Damiano, R., Patti, V., & Cambria, E. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.11.002 · Cited by 15

The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength

Thelwall, M. A. (2016) · Understanding complex systems · doi:10.1007/978-3-319-43639-5_7 · Cited by 213

Heart and Soul: Sentiment Strength Detection in the Social Web with SentiStrength 1

Thelwall, M. (2013)

Investigation of sentiment strength detection in twitter texts

Orešković, M. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Sentiment strength detection in short informal text

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449

Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words

Thelwall, M. A., & Buckley, K. A. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22872 · Cited by 140