Using graph-theoretic methods for text classification

Shanavas, Niloofer; Wang, Hui; Lin, Zhiwen; Hawe, Glenn I.

doi:10.1142/9789813273238_0077

Cited by 1 publication

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“…Before text classification, it is necessary to represent the text obtained in numeric form so the ML classifier can read it, which is known as the feature engineering phase. In text classification generally and SA specifically, significant attention is given to preprocessing operations and feature engineering methods to identify and extract representative features (terms) for natural language documents [9]. The extracted features are represented as feature vectors, where each term is associated with its own weight.…”

Section: Introductionmentioning

confidence: 99%

TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis

Alshehri

Algarni

2023

Electronics

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In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.

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Section: Introductionmentioning

confidence: 99%