Abstract
With increasing globalization, communication among people of diverse cultural backgrounds is also taking place to a very large extent in the present era. Issues like language diversity in various parts of the world can lead to hindrance in communication. The usage of social media and user-generated material has grown at an exponential rate and existing supervised sentiment polarity classification techniques need labelling for the training dataset. In this study, two problems have been analyzed. First, sentiment analysis of the Twitter dataset and sense disambiguation of morphologically rich Hindi language. A rule-based fuzzy logics-based system for self-supervised sentiment classification was used to compute and analyze the self-supervised or completely unsupervised sentiment categorization of a social-media dataset using three types of lexicons. The combination of fuzzy with three different types of lexicons gives sentiment analysis a new path. The unsupervised fuzzy rules integrate the fuzziness of both negative as well as positive scores, and fuzzy logic-based systems can cope with ambiguity and vagueness. The fuzzy-system uses an unsupervised/self-supervised fuzzy rule-based technique to identify text using natural language processing (NLP) and sense of word. We compared the results of fuzzy rule based self-supervised sentiment classification by using three types of lexicons on five different datasets, with unsupervised as well as supervised sentiment classification techniques. Second, using cross-lingual sense embedding rather than cross-lingual word embedding resolves the ambiguity issue. The word sense embeddings are produced for the source languages to learn multiple or various senses of the words. Different evaluation metrics depict an improved performance for English-Hindi language.
- . 2019. Deep learning-based sentiment classification of evaluative text based on multi-feature fusion. Information Processing & Management 56, 4 (2019), 1245–1259.Google Scholar
Digital Library
- . 2015. Sentiment analysis using common-sense and context information. Computational Intelligence and Neuroscience (2015).Google Scholar
Digital Library
- . 2019. All-in-one: Emotion, sentiment and intensity prediction using a multi-task ensemble framework. IEEE Transactions on Affective Computing (2019).Google Scholar
- . 2014. Sentiment analysis: Towards a tool for analysing real-time students feedback. In 2014 IEEE 26th International Conference on Tools with Artificial Intelligence. IEEE. 419–423.Google Scholar
Digital Library
- : https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data.Google Scholar
- . 2017. Unsupervised neural machine translation. arXiv preprint arXiv:1710.11041.Google Scholar
- . 2018. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. arXiv preprint arXiv:1805.06297.Google Scholar
- 2021. Senti-eSystem: A sentiment-based eSystem-using hybridized fuzzy and deep neural network for measuring customer satisfaction. Software: Practice and Experience 51, 3 (2021), 571–594.Google Scholar
Cross Ref
- . 2010. SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In LREC 10, 2010, 2200–2204.Google Scholar
- . 2010. Robust sentiment detection on Twitter from biased and noisy data. In Coling 2010: Posters. 36–44.Google Scholar
- . 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65–72.Google Scholar
Digital Library
- . 2007. Scaling learning algorithms towards AI. Large-Scale Kernel Machines 34, 5 (2007), 1–41.Google Scholar
- . 2021. Low resource neural machine translation: Assamese to/from other Indo-Aryan (Indic) languages. Transactions on Asian and Low-Resource Language Information Processing 21, 1 (2021), 1–32.Google Scholar
- . 2018. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceedings of the AAAI Conference on Artificial Intelligence 32, 1 (2018).Google Scholar
Cross Ref
- . 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.Google Scholar
- . 2002. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In Proceedings of the Second International Conference on Human Language Technology Research. 138–145.Google Scholar
Digital Library
- . 1997. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29, 2 (1997), 103–130.Google Scholar
Digital Library
- . 2017. A fast and accurate rule-base generation method for Mamdani fuzzy systems. IEEE Transactions on Fuzzy Systems 26, 2 (2017), 715–733.Google Scholar
Cross Ref
- . 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1, 12 (2009), 2009.Google Scholar
- . 2022. Sentiment analysis and sarcasm detection from social network to train health-care professionals. World Journal of Engineering 19 1 (2022), 124–133.Google Scholar
Cross Ref
- . 2014. 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 8, 1 (2014).Google Scholar
Cross Ref
- : https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews.Google Scholar
- . 1997. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on Automatic Control 42, 10 (1997), 1482–1484.Google Scholar
Cross Ref
- . 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759.Google Scholar
- . 2015. Improved lexicon-based sentiment analysis for social media analytics. Security Informatics 4, 1 (2015), 1–13.Google Scholar
Cross Ref
- . 2019. Improving unsupervised word-by-word translation with language model and denoising autoencoder. arXiv preprint arXiv:1901.01590.Google Scholar
- . 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.Google Scholar
- . 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out. 74–81.Google Scholar
- . 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5, 1 (2012), 1–167.Google Scholar
Digital Library
- . 2015. Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets and Systems 258 (2015), 5–38.Google Scholar
Cross Ref
- . 2019. Behaviour of players on IPL based on fuzzy C means. International Journal of Innovative Technology and Exploring Engineering 8, 9S (2019), 150--154.Google Scholar
- . 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7, 1 (1975), 1–13.Google Scholar
Cross Ref
- . 2018. An ANEW based fuzzy sentiment analysis model. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. 1–7.Google Scholar
Digital Library
- . 2013. Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Syst. Appl. 40, 2 (2013), 621–633.Google Scholar
Digital Library
- . 2011. SentiFul: A lexicon for sentiment analysis. IEEE Transactions on Affective Computing 2, 1 (2011), 22–36.Google Scholar
Digital Library
- . 2011. AFINN. Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby.Google Scholar
- . 2014. Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior 31 (2014), 527–541.Google Scholar
Digital Library
- . 2020. Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access 8 (2020), 111141–111158.Google Scholar
Cross Ref
- . 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. 311–318.Google Scholar
Digital Library
- . 2018. Sentiment analysis of Twitter corpus related to artificial intelligence assistants. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA). IEEE. 495–498.Google Scholar
Cross Ref
- . 2020. SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets. arXiv e-prints, arXiv-2008.Google Scholar
- . 2017. Making sense of word embeddings. arXiv preprint arXiv:1708.03390.Google Scholar
- . 2013. Recognizing emotion presence in natural language sentences. In International Conference on Engineering Applications of Neural Networks. Springer, Berlin. 30–39.Google Scholar
Cross Ref
- . 2022. Ranking tourist attractions through online reviews: A novel method with intuitionistic and hesitant fuzzy information based on sentiment analysis. International Journal of Fuzzy Systems 24, 2 (2022), 755–777.Google Scholar
Cross Ref
- . 2022. Indian sign language recognition using ensemble based classifier combination. Macromolecular Symposia 401, 1 (2022), 2100286.Google Scholar
Cross Ref
- . 2018. An ensemble classification system for Twitter sentiment analysis. Procedia Computer Science 132 (2018), 937–946.Google Scholar
Cross Ref
- . 2013. IVTURS: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection. IEEE Transactions on Fuzzy Systems 21, 3 (2013), 399–411.Google Scholar
Digital Library
- . 2021. Fuzzy logic applied to opinion mining: A review. Knowledge-Based Systems 222 (2021), 107018.Google Scholar
Cross Ref
- . 2020. Clustering Yelp's sentiment data through various approaches and estimating the error rate. Materials Today: Proceedings (2020). .Google Scholar
Cross Ref
- . 2017. Exploiting expectation maximization algorithm for sentiment analysis of product reviews. In 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE. 390–396.Google Scholar
Cross Ref
- . 2021. An end-to-end shape-preserving point completion network. IEEE Computer Graphics and Applications 41, 3 (2021), 124–138.Google Scholar
Cross Ref
- . 2022. Low-resource neural machine translation: Methods and trends. Transactions on Asian and Low-Resource Language Information Processing.Google Scholar
Digital Library
- . 2019. UAIC at SemEval-2019 Task 3: Extracting much from little. In Proceedings of the 13th International Workshop on Semantic Evaluation. 355–359.Google Scholar
Cross Ref
- . 2021. Aspect-based sentiment analysis of mobile phone reviews using LSTM and fuzzy logic. International Journal of Data Science and Analytics 12, 4 (2021), 355–367.Google Scholar
Cross Ref
- . 2009. Fluency, adequacy, or HTER? Exploring different human judgments with a tunable MT metric. In Proceedings of the Fourth Workshop on Statistical Machine Translation. 259–268.Google Scholar
Digital Library
- : https://www.kaggle.com/yash612/stockmarket-sentiment-dataset.Google Scholar
- . 2007. SemEval-2007 task 14: Affective text. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007). 70–74.Google Scholar
Cross Ref
- 2021. Unsupervised neural machine translation for similar and distant language pairs: An empirical study. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) 20, 1 (2021), 1–17.Google Scholar
Digital Library
- . 2013. Interpreting the public sentiment variations on Twitter. IEEE Transactions on Knowledge and Data Engineering 26, 5 (2013), 1158–1170.Google Scholar
- . 2012. Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology 63, 1 (2012), 163–173.Google Scholar
Digital Library
- . 2016. Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications 57 (2016), 117–126.Google Scholar
Digital Library
- . 2021. Fuzzy natural logic for sentiment analysis: A proposal. International Symposium on Distributed Computing and Artificial Intelligence. Springer, Cham, 2021.Google Scholar
- . 2019. Fuzzy rule based unsupervised sentiment analysis from social media posts. Expert Systems with Applications 138 (2019), 112834.Google Scholar
Cross Ref
- . 2020. Fuzzy interpretation of word polarity scores for unsupervised sentiment analysis. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE. 1–6.Google Scholar
Cross Ref
- . 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017), 30.Google Scholar
- . 2017. Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek. In 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE. 266–269.Google Scholar
Cross Ref
- . 2013. YouTube movie reviews: Sentiment analysis in an audio-visual context. IEEE Intelligent Systems 28, 3 (2013), 46–53.Google Scholar
Digital Library
- . 2020. E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework. Information Processing & Management 57, 5 (2020), 102221.Google Scholar
Cross Ref
- . 2020. Sentiment analysis of financial news using unsupervised approach. Procedia Computer Science 167 (2020), 589–598.Google Scholar
Cross Ref
- . 2017. Two simple and effective ensemble classifiers for Twitter sentiment analysis. In 2017 Computing Conference. IEEE. 1386–1393.Google Scholar
Cross Ref
- . 2020. Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8 (2020), 23522–23530.Google Scholar
Cross Ref
- . 2018. Social media contents based sentiment analysis and prediction system. Expert Systems with Applications 105 (2018), 102–111.Google Scholar
Cross Ref
- . 2015. Fuzzy logic—a personal perspective. Fuzzy Sets and Systems, 281, 4–20.Google Scholar
Digital Library
Index Terms
Rule Based Fuzzy Computing Approach on Self-Supervised Sentiment Polarity Classification with Word Sense Disambiguation in Machine Translation for Hindi Language
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