Abstract
The characteristic of context dependency in Urdu words needs to be handled carefully while performing Urdu sentiment analysis. In this research, an already constructed Urdu sentiment lexicon of positive and negative words is further expanded by the addition of context-dependent words. These context-dependent words are used with or without conjunctions. Rules are formulated for assigning polarities to those context-dependent words that are surrounded by the positive or negative words. These rules were incorporated in the Urdu sentiment analyzer. Fusion of these rules for handling context-dependent words and the expanded Urdu sentiment lexicon resulted in increasing the accuracy of the Urdu sentiment analyzer from 83.43% to 89.03% with 0.8655 precision, 0.9053 recall, and 0.8799 F-measure, which is a statistically significant improvement.
- [1] . 2016. Affective computing and sentiment analysis. IEEE Intelligent Systems 31, 2 (2016), 102–107.Google Scholar
Digital Library
- [2] . 2016. Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering 28, 3 (2016), 813–829.Google Scholar
Digital Library
- [3] . 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool.Google Scholar
Cross Ref
- [4] . 2016. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems 108 (2016), 42–49.Google Scholar
Digital Library
- [5] . 2016. Visual and textual sentiment analysis of a microblog using deep convolutional neural networks. Algorithms 9, 2 (2016), 41.Google Scholar
Cross Ref
- [6] . 2010. Sentence-level and document-level sentiment mining for arabic texts. In Proceedings of the 2010 IEEE International Conference on Data Mining Workshops (ICDMW’10).Google Scholar
Digital Library
- [7] . 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL’02). 417–424.Google Scholar
- [8] . 2021. A survey on sentiment analysis in Urdu: A resource-poor language. Egyptian Informatics Journal 22, 1 (2021), 53–74.Google Scholar
Cross Ref
- [9] 2020. Opinion mining and summarization: A comprehensive review. Journal of Communication Technologies and Robotics Applications 11, 1 (2020), 76–96.Google Scholar
- [10] . 2014. Acquiring commonsense knowledge for sentiment analysis through human computation. In Proceedings of the 23rd International Conference on World Wide Web (WWW’14). 225–226.Google Scholar
Digital Library
- [11] , D. Miao, and . 2014. Context-Dependent Sentiment Classification Using Antonym Pairs and Double Expansion. Springer International.Google Scholar
- [12] . 2014. Aspect based summarization of context dependent opinion words. Procedia Computer Science 35 (2014), 166–175.Google Scholar
- [13] 2009. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics 35, 3 (2009), 399–433.Google Scholar
Digital Library
- [14] 2016. Sentiment classification of context dependent words. In Proceedings of the International Conference on ICT for Sustainable Development.Google Scholar
Cross Ref
- [15] . 2010. Sentiment analysis and subjectivity. In Handbook of Natural Language Processing (2nd ed.), Nitin Indurkhya and Fred J. Damerau (Eds.). CRC Press, Boca Raton, FL, 627–666.Google Scholar
- [16] . 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1–2 (2008), 1–135.Google Scholar
Digital Library
- [17] . 2018. Lexicon-based approach outperforms supervised machine learning approach for Urdu sentiment analysis in multiple domains. Telematics and Informatics 35, 8 (2018), 2173–2183.Google Scholar
Cross Ref
- [18] . Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis. Expert Systems 35, 6 (2018), 1–12.Google Scholar
Cross Ref
- [19] . 2018. Urdu sentiment analysis using supervised machine learning approach. International Journal of Pattern Recognition and Artificial Intelligence 32, 1 (2018), 1851001–1851015.Google Scholar
Cross Ref
- [20] . 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining. IEEE, Los Alamitos, CA.Google Scholar
Cross Ref
- [21] . 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05).Google Scholar
Digital Library
- [22] 2008. A holistic lexicon-based approach to opinion mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM ’08).Google Scholar
- [23] . 2010. YSC-DSAA: An approach to disambiguate sentiment ambiguous adjectives based on SAAOL. In Proceedings of the 5th International Workshop on Semantic Evaluation. 440–443.Google Scholar
- [24] . 2010. HITSZ_CITYU: Combine collocation, context words and neighboring sentence sentiment in sentiment adjectives disambiguation. In Proceedings of the 5th International Workshop on Semantic Evaluation. 448–451.Google Scholar
- [25] . 2015. Word polarity disambiguation using Bayesian model and opinion-level features. Cognitive Computation 7 (2015), 369–380.Google Scholar
Cross Ref
- [26] . 2009. Delta TFIDF: An improved feature space for sentiment analysis. In Proceedings of the 3rd AAAI International Conference on Weblogs and Social Media.Google Scholar
- [27] . 2012. An information theoretic approach to sentiment polarity classification. In Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality (WebQuality’12). ACM, New York, NY, 35–40.Google Scholar
Digital Library
- [28] . 2013. Revised mutual information approach for German text sentiment classification. In Proceedings of the 22nd International Conference on World Wide Web Companion.Google Scholar
Digital Library
- [29] Chaoticity.com. 2016. Urdu Sentiment Lexicon. Retrieved July 5, 2016 from http://chaoticity.com/urdusentimentlexicon/.Google Scholar
- [30] Data Science Lab. 2016. Multilingualsentiment. Retrieved July 15, 2016 from https://sites.google.com/site/datascienceslab/projects/multilingualsentiment.Google Scholar
- [31] Urdu lughat. 2016. XXX. Retrieved May 20, 2016 from http://urdulughat.info/.Google Scholar
- [32] Center for Language Engineering. 2016. News. Retrieved July 8, 2016 from http://www.cle.org.pk/.Google Scholar
- [33] Center for Language Engineering. 2016. POS tagset. Retrieved June 2016 from http://www.cle.org.pk/software/langproc/POStagset.htm/Google Scholar
- [34] XXX. XXX. XXX. Retrieved XXX from https://urdusentidata.wordpress.com//2019.Google Scholar
- [35] . 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10 (1998), 1895–1923.Google Scholar
Digital Library
- [36] . 2013. An evaluation of classification algorithms using Mc Nemar's test. Advances in Intelligent Systems and Computing 201 (2013), 15–26.Google Scholar
Cross Ref
- [37] . 2018. Opinion within opinion: Segmentation approach for Urdu sentiment analysis. International Arab Journal of Information Technology 15, 1 (2018), 21–28.Google Scholar
- [38] . 2017. Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PLoS One 12, 2 (2017), 1–22.Google Scholar
Cross Ref
- [39] . 2011. Adjectival phrases as the sentiment carriers in Urdu. Journal of American Science 7, 3 (2011), 644–652.Google Scholar
- [40] . 2014. Urdu opinion mining system (RUOMiS). Computer Science & Engineering: An International Journal 4, 6 (2014), 1–9.Google Scholar
Cross Ref
- [41] . 2014. Associating targets with SentiUnits: A step forward in sentiment analysis of Urdu text. Artificial Intelligence Review 41, 4 (2014), 535–561.Google Scholar
Digital Library
- [42] . 2016. Lexicon-based sentiment analysis for Urdu language. In Proceedings of the 6th International Conference on Innovative Computing Technology (INTECH’16).Google Scholar
Cross Ref
- [43] . 2019. Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource-poor language. Expert Systems 36, 3 (2019), e12397.Google Scholar
Cross Ref
Index Terms
An Intelligent Unsupervised Approach for Handling Context-Dependent Words in Urdu Sentiment Analysis
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