An opinionated sentiment analysis using a rule-based method

Web Intelligence, 2019

Sentiment analysis is one of the most important and interesting tasks in natural languages. A number of resources and tools have been developed for sentiment analysis of English, Turkish, Russian and other languages. Unfortunately, there were no data and tools available for sentiment analysis in Kazakh. The Dictionary of Kazakh sentiment words has been created during this study. In this work, we described the rule-based method using a dictionary of emotional words for sentiment analysis of texts in the Kazakh language, based on the morphological rules and ontological model. We studied the texts in Kazakh and determined the parts of speech that define the text mood. Based on the conducted studies, a lot of phrases were identified as determining the text polarity. This paper is an extended version of the paper published in [in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, 2017, pp. 669-677]. In addition to the original material, the paper includes additional rules for determining sentiment on a 5-point scale.

International Journal of Big Data Intelligence, 2014

Due to the evolution of Web 2.0 technology, internet users are more capable of posting their comments and reviews to express their opinions and feelings about everything. Hence, the necessity of automatically identifying the polarity (be it positive, negative, or neutral) of these comments arose and new interdisciplinary field called sentiment analysis (SA) emerged. Unluckily, many studies were conducted on the English language whereas those on the Arabic language are quite few. In addition, the publicly available datasets and testing tools for SA of Arabic text are rare. In this paper, a relatively large dataset of Arabic comments is manually collected and annotated. The source is one of the most widely used social networks in the Arab world, Yahoo!-Maktoob. A comprehensive analysis of this dataset is presented and two popular classifiers, support vector machine (SVM) and Naive Bayes (NB) are used for empirical experimentations. The results show that SVM outperforms NB and achieves a 64% accuracy level.

Scientific journal of Astana IT University, 2024

Sentiment analysis is increasingly pivotal in natural language processing (NLP), crucial for deciphering public opinions across diverse sectors. This research conducts a comparative examination of rule-based and machine learning (ML) methods in sentiment analysis, specifically targeting the Kazakh language. Given the Kazakh language's limited exposure in computational linguistics, the study meticulously evaluates datasets from news articles, literature, and Amazon product reviews, aiming to compare the efficiency, adaptability, and overall performance of these distinct approaches. Employing a detailed set of evaluation metrics such as accuracy, precision, recall, and computational efficiency, the study provides a comprehensive analysis of the strengths and limitations of rule-based techniques versus ML models like Logistic Regression, Multinomial Naive Bayes, Decision Trees, Random Forest, and XGBoost. The findings suggest rule-based methods excel in identifying nuanced emotional expressions within literary texts, while ML models demonstrate superior adaptability and robustness, particularly effective in handling the linguistic variations found in news and reviews. Despite the strengths identified, the study also reveals significant limitations of the rulebased approach, especially in broader contexts beyond literary analysis. This highlights an imperative for future research to integrate sentiment dictionaries or domain-specific lexicons that cater to a wider array of linguistic styles, potentially enhancing sentiment analysis tools' applicability in Kazakh and similar less-studied languages.

Communications in computer and information science, 2023

With the proliferation of digital content, it is increasingly important to understand the sentiment of the information that is being shared. Sentiment Analysis, the procedure of identifying the emotional tone of a given text, has become a crucial area of research in Natural Language Processing (NLP). However, performing sentiment analysis on Bodo language texts is challenging due to inadequate labeled data and the complexity of the language. The amount of resources and research available for Bodo language is quite limited compared to more widely spo ken languages, such as English or Hindi. This study proposes a lexicon based approach for sentiment analysis of Bodo language texts, which is a low-resource language spoken in India. For our purpose, we have con sidered sentences and words from various social sites and then created a Bodo sentiment lexicon through manual labeling of Bodo language words and sentences with their sentiment polarity (positive, negative, or neutral). Results from evaluating the approach on a dataset of Bodo language texts show an accuracy of 70%. This lexicon-based approach is a simple and effective method for sentiment analysis of Bodo language texts and can be used to enhance the performance of other NLP tasks such as text classification and opinion mining.

In this paper, we present a concise introduction to Sentiment Analysis. We introduce a multilingual lexicon-based SA application, Lingmotif, developed by the Tecnolengua team at the Universidad de Málaga (Spain). Lingmotif is a lexicon-based, multi-platform desktop application for Sentiment Analysis. This software detects sentiment-laden words and multi-word expressions and returns a detailed analysis of its sentiment. In this paper, we provide a description of the tool's interface along with a brief proposal for its application in education and English Language Teaching.

The tasks that falls under the errands that takes after Natural Language Processing approaches includes Named Entity Recognition, Information Retrieval, Machine Translation, and so on. Wherein Sentiment Analysis utilizes Natural Language Processing as one of the way to locate the subjective content showing negative, positive or impartial (neutral) extremity (polarity). Due to the expanded utilization of online networking sites like Facebook, Instagram, Twitter, Sentiment Analysis has increased colossal statures. Examination of sentiments helps organizations, government and other association to extemporize their items and administration in view of the audits or remarks. This paper introduces an Innovative methodology that investigates the part of lexicalization for Arabic Sentiment examination. The system was put in place with two principles rules– “equivalent to” and “within the text” rules. The outcomes subsequently accomplished with these rules methodology gave 89.6 % accuracy when tried on baseline dataset, and 50.1 % exactness on OCA, the second dataset. A further examination shows 19.5 % in system1 increase in accuracy when compared with baseline dataset.

This paper presents an innovative approach that explores the role of lexicalization for Arabic sentiment analysis. Sentiment Analysis in Arabic is hindered due to lack of resources, language in use with sentiment lexicons, pre-processing of dataset as a must and major concern is repeatedly following same approaches. One of the key solution found to resolve these problems include applying the extension of lexicon to include more words not restricted to Modern Standard Arabic. Secondly, avoiding pre-processing of dataset. Third, and the most important one, is investigating the development of an Arabic Sentiment Analysis system using a novel rule-based approach. This approach uses heuristics rules in a manner that accurately classifies tweets as positive or negative. The manner in which a series of abstraction occurs resulting in an end-to-end mechanism with rule-based chaining approach. For each lexicon, this chain specifically follows a chaining of rules, with appropriate positioning and prioritization of rules. Expensive rules in terms of time and effort thus resulted in outstanding results. The results with end-to-end rule chaining approach achieved 93.9% accuracy when tested on baseline dataset and 85.6% accuracy on OCA, the second dataset. A further comparison with the baseline showed huge increase in accuracy by 23.85%.

The Web 2.0 refers to the second generation of World Wide Web (WWW). Web 2.0 allows Internet users to collaborate and share information online, and therefore create large virtual societies. Web 2.0 includes social network sites, Wikis, Blogs, Web services, podcasting, Multimedia sharing services ...etc. Arab users of social network sites (Facebook and Twitter) generate daily a large volume of Arabic and English textual reviews related to different social, political and scientific subjects. These reviews could be about different products, political events, sport teams, economics, video clips, restaurants, books, actors/actress, new films and songs, universities ...etc. This large volume of different Arabic and English textual reviews cannot be analyzed manually. Therefore sentiment analysis is used to identify sentiments with their subjectivity from this huge volume of reviews. In order to conduct this study a small dataset consisting of 4,050 Arabic and English reviews were collected. Three polarity dictionaries were also created (Arabic, English, and Emoticons). The collected dataset and those dictionaries were used to conduct a comparison between two free online sentiment analysis tools (SocialMention

The British University in Dubai (BUiD), 2015

Named Entity Recognition, Question Answering, Information Retrieval, Machine Translation, etc. fall under the tasks that follow Natural Language Processing approaches wherein Sentiment Analysis uses Natural Language Processing as one of the means to find the subjective text indicating negative, positive or neutral polarity. The united approach of text mining and natural language processing, termed to be as Sentiment Analysis has gained huge heights due to the increased use of social media websites like Facebook, Instagram, Twitter to name a few. Sentiment Analysis is a growing field and nevertheless a lot of research is done in English when compared to Arabic language. Analysis of Sentiments helps companies, government and other organization to improvise their products and service based on the reviews or comments. This dissertation not only depicts the various challenges faced by Arabic Natural Language processing in the Sentiment Analysis task, but this dissertation presents an Innovative approach that explores the role of lexicalization for Arabic sentiment analysis. Sentiment Analysis in Arabic is hindered due to lack of resources, language in use with sentiment lexicons, pre-processing of dataset as a must and major concern is repeatedly following same approaches. One of the key solution found to resolve these problems include applying the extension of lexicon to include more words not restricted to Modern Standard Arabic. Secondly, avoiding pre-processing of dataset. Third, and the most important one, is investigating the development of an Arabic Sentiment Analysis system using a novel rule-based approach. This approach uses heuristics rules that is triggered based on end-to-end mechanism of a particular word in a manner that accurately classifies the tweets as positive or negative. The manner in which a series of abstraction occurs resulting in an end to end rule-based chaining approach. For each lexicon this chain specifically follows a chaining of rules (i.e. rule A chains with rule B and if required rule C and so on), with appropriate positioning and prioritization of rules. Expensive rules in terms of time and effort thus resulted in outstanding results. Experiments were conducted on two dataset. They are chosen for a number of good reasons, including their availability and successfully used by other researches, richness and sufficient to come to a conclusion, and provision with electronic resources such as lexicon. Two set of 4 experiments were done. The first set of experiment was done only with two rules-"equal to" and "within the text". The second set of experiment was done with rule chaining mechanism. The results thus achieved with end to end rule chaining approach achieved 93.9% accuracy when tested on one dataset, which is considered the baseline, and 85.6% accuracy on OCA, the second dataset. A further comparison with the baseline showed huge increase in accuracy by 23.85%.

International Journal of Scientific Research in Science, Engineering and Technology, 2020

Opinions are personal judgment on entity. This is not only true for individuals but also true for organizations. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. The process of sentiment mining involves categorizing an opinionated document into predefined categories such as positive, negative or neutral based on the sentiment terms that appear within the opinionated document. For this study text document corpus is prepared by the researcher encompassing different movies ‘reviews and Various techniques of text pre-processing including tokenization, normalization, stop word removal and stemming are used for this system(sentiment mining model for opinionated afaan Oromo texts). The experiment shows that the performance is on the average 0.849(84.9%) precision and 0.887(88.7%) recall. The challenging tasks in the study are handling synonymy and inability of the stemmer algorithm to all word variants, and ambiguity of words in the language. The performance the system can be increased if stemming algorithm is improved, standard test corpus is used, and thesaurus is used to handle polysemy and synonymy words in the language.