Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering: An Innovative Multilingual Approach for Social Media Reviews
Source abstract
Abstract
Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexicon is developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentiment scores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. In the second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis of Roman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrieval metrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The results showcase the potential for improving software engineering tasks related to user feedback analysis and product development.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Web Data Mining and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
Show source-provided references
1. A lexicon enhanced collaborative network for targeted financial sentiment analysis10.1016/j.ipm.2022.103187 ↗
2. State of the art: A review of sentiment analysis based on sequential transfer learning10.1007/s10462-022-10183-8 ↗
3. H. Saggion et al., “Findings of the TSAR-2022 shared task on multilingual lexical simplification,” arXiv preprint arXiv:2302.02888, 2023.10.18653/v1/2022.tsar-1.31 ↗
4. Lexical complexity prediction: An overview10.1145/3557885 ↗
5. A defect detection method for the primary stage of software development
6. Peak anomaly detection from environmental sensor-generated watershed time series data
7. A deep hybrid model for recommendation by jointly leveraging ratings, reviews and metadata information10.1016/j.engappai.2020.104066 ↗
8. Improving sentiment analysis in election-based conversations on Twitter with ElecBERT language model
9. Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis10.1007/s10660-022-09630-z ↗
10. An improved model for sentiment analysis on luxury hotel review10.1111/exsy.12580 ↗
11. Customer expectations in the hotel industry during the COVID-19 pandemic: A global perspective using sentiment analysis
12. Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews10.1016/j.tourman.2022.104707 ↗
13. Evaluating new energy vehicles by picture fuzzy sets based on sentiment analysis from online reviews10.1007/s10462-022-10217-1 ↗
14. COVID-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset10.1016/j.eswa.2022.118715 ↗
15. Mind games: A temporal sentiment analysis of the political messages of the internet research agency on Facebook and Twitter10.1177/14614448211014355 ↗
16. A semi-supervised approach for aspect category detection and aspect term extraction from opinionated text10.32604/cmc.2023.040638 ↗
17. Valence-dependent mutation in lexical evolution10.1038/s41562-022-01483-8 ↗
18. Sentiment analysis of text reviews using lexicon-enhanced bert embedding (LeBERT) model with convolutional neural network10.3390/app13031445 ↗
19. Streaming news sequential evolution model based on distributed representations
20. Identifying hot topic trends in streaming text data using sequential evolution model based on distributed representations10.1109/ACCESS.2023.3312764 ↗
21. On the utilization of non-quality assessed literature in software engineering research
22. Counteracting sociocultural barriers in global software engineering using group activities
23. Affinity propagation-based hybrid personalized recommender system10.1155/2022/6958596 ↗
24. Classification of Parkinson’s disease in patch-based MRI of substantia nigra10.3390/diagnostics13172827 ↗
25. Valuating requirements arguments in the online user’s forum for requirements decision-making: The CrowdRE-VArg framework10.1002/spe.3137 ↗
26. Exploring and mining rationale information for low-rating software applications
27. Conceptualising, extracting and analysing requirements arguments in users’ forums: The CrowdRE-Arg framework
28. Analysis of requirements-related arguments in user forums
29. SENTIWORDNET 3.0: An enhanced lexical resource for sentiment analysis and opinion mining
30. Emotional sentiment analysis of social media content for mental health safety10.1007/s13278-022-01000-9 ↗
31. D. Bor, B. S. Lee, and E. J. Oughton, “Quantifying polarization across political groups on key policy issues using sentiment analysis,” arXiv preprint arXiv:2302.07775, 2023.
32. A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis10.1186/s40537-022-00680-6 ↗
33. KSCB: A novel unsupervised method for text sentiment analysis10.1007/s10489-022-03389-4 ↗
34. Natural language processing implementation for sentiment analysis on tweets
35. Machine learning sentiment analysis, COVID-19 news and stock market reactions10.1016/j.ribaf.2023.101881 ↗
36. Interaction strategies in online learning: Insights from text analytics on iMOOC10.1007/s10639-022-11270-7 ↗
37. The importance of lexicon in language learning
38. Top-down information shapes lexical processing when listening to continuous speech
39. M. Heitmann, C. Siebert, J. Hartmann, and C. Schamp, “More than a feeling: Benchmarks for sentimentanalysis accuracy,” SSRN Electr. J., vol. 40, no. 1, pp. 75–87, 2020.10.1016/j.ijresmar.2022.05.005 ↗
40. Types of lexical meaning according to the formation
41. Predicting scalar implicature interpretations from lexical knowledge10.1044/2022_JSLHR-22-00083 ↗
42. Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy10.1016/j.compbiomed.2023.106876 ↗
43. Post-pandemic cities: An urban lexicon of accelerations/decelerations10.1111/tran.12607 ↗
44. S. H. Muhammad et al., “SemEval-2023 Task 12: Sentiment analysis for African languages (AfriSenti-SemEval),” arXiv preprint arXiv:2304.06845, 2023.10.18653/v1/2023.semeval-1.315 ↗
45. S. H. Muhammad et al., “AfriSenti: A Twitter sentiment analysis benchmark for african languages,” arXiv preprint arXiv:2302.08956, 2023.
46. D. M. W. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061, 2020.
47. Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques
48. An unsupervised lexical normalization for Roman Hindi and Urdu sentiment analysis10.1016/j.ipm.2020.102368 ↗
49. Sentiment analysis of social media content in pashto language using deep learning algorithms10.53106/160792642022122307021 ↗