Inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets

Patil, Rupali; Kolhe, Satish R.

doi:10.1007/978-3-031-07005-1_23

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…The authors in [91] combined the dictionary lookup and rulebased approaches. Jabbar et al [16] proposed a multi-step Urdu stemmer that works in two phases: In the first phase, compound words are extracted from the text fragment using punctuation marks as well as stop words as a delimiter, for instance, the Urdu sentence Mahalingam, [92] presented a stemmer named Bruteporter: A hybrid stemmer for the English language, that used Wordnet, and Modified Porter [38] Algorithm.…”

Section: E Hybrid Approachesmentioning

confidence: 99%

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Jabbar,

Iqbal,

Tamimy

et al. 2023

IEEE Access

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The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.

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Section: E Hybrid Approachesmentioning

confidence: 99%

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Jabbar,

Iqbal,

Tamimy

et al. 2023

IEEE Access

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“…For this reason, the issue has been handled as a regression and classification challenge. Since the goal of this study is to determine the effect and trend of COVID-19, machine learning classifiers such as support vector machine (SVM) [12] and random forest (RF) [13], k-nearest neighbor (KNN), and decision tree (DT) were used. For feature extraction, the emotions of the tweets were acquired in terms of negative, positive, and neutral tweets.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

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Building Marathi SentiWordNet

Patil¹,

Kolhe²

2023

Communications in Computer and Information Science

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Inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets

Cited by 5 publications

References 22 publications

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Building Marathi SentiWordNet

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