Improving stemming for Assamese information retrieval

Gogoi, Arjun; Baruah, Nomi; Sarma, Shikhar Kumar; Phukan, Rakhee D.

doi:10.1007/s41870-021-00718-7

Cited by 9 publications

(2 citation statements)

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“…And stemming, process of reducing words to their root or base form. For example, "jumping" and "jumps" would both be stemmed to "jump" [25].…”

Section: Data Preprocessingmentioning

confidence: 99%

Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine

Sutriawan,

Muljono,

Khairunnisa

et al. 2024

MMEP

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The use of online movie streaming media has increased significantly, particularly among movie enthusiasts. However, fan comments are frequently informal and comprise informal language, subjectivity, and contexts that reflect their preferences. A significant challenge in sentiment analysis of movie reviews is how to classify sentiments in reviews that are often unstructured and subjective. This study aims to improve the accuracy of sentiment classification in movie reviews by proposing several methods, including a hybrid TF-IDF+N-Gram model that can extract pertinent information from word and phrase sequences in reviews. Then, feature selection with Information Gain (IG) is performed to identify the most informative sentiment classification features. This strategy seeks to overcome informal language and noise to improve review context comprehension. The results demonstrated a significant gain in the accuracy of sentiment classification. TFIDF+Bigram+IG achieved 78% accuracy (up 8% from 70% previously), and TFIDF+Trigram+IG achieved 66% accuracy (up 22% from 44% previously). Using this hybrid model, the study significantly enhanced the accuracy of sentiment classification, thereby enhancing the performance of SVM in the face of complex movie evaluations.

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“…And stemming, process of reducing words to their root or base form. For example, "jumping" and "jumps" would both be stemmed to "jump" [25].…”

Section: Data Preprocessingmentioning

confidence: 99%

Improving Performance Sentiment Movie Review Classification Using Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine

Sutriawan,

Muljono,

Khairunnisa

et al. 2024

MMEP

View full text Add to dashboard Cite

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“…Stemmers are important for developing Information Retrieval (IR) systems. A Gogoi, N Baruah, SK Sarma and RD Phukan [21] Named entity Recognition (NER) plays a very crucial role in developing information extraction system, machine translation system or a question-answering system. G Talukdar, PP Borah and A Baruah [22] have proposed an NER system in Assamese language emphasizing on individual features contributing towards recognition of entities like person, location or organization.…”

Section: Related Workmentioning

confidence: 99%

N-Gram based Assamese Question Pattern Extraction and Probabilistic Modelling

Rita Chakraborty

2024

jes

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N-gram probabilities provide valuable information in understanding, processing, and modelling various natural language processing tasks. They assign probabilities to the sequences of words and subsequently to the whole sentence. Such information is very essential to make more accurate predictions in machine learning based systems. Here in this paper we worked on finding Parts-of-Speech (PoS) sequence based Assamese question patterns. We derived the unique bi-grams and tri-grams of PoSs occurring in the patterns and also extracted the probabilities of them. We then tried to find the unique PoS patterns of Assamese questions. We also have tried to incorporate the probabilities of unique bi-grams and tri-grams and the combined bi-grams and tri-grams probabilities of all patterns. Our work is a novel approach of finding the probabilities of bi-grams and tri-grams of the patterns occurring in Assamese questions.

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