BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations

Parupalli, Sreekavitha; Rao, Vijjini Anvesh; Mamidi, Radhika

doi:10.18653/v1/p18-3014

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Xue et al proposed a classification model based on convolutional neural network and gating mechanism [10]. Parupalli et al constructed a corpus with systematic annotation [11], which supports the use of word-level annotation to enhance emotion analysis tasks. Angelidis et al proposed an attention-based polarity scoring method for positive and negative text fragments [12].…”

Section: Introductionmentioning

confidence: 99%

A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines

Jiang

2022

Scientific Programming

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With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.

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Section: Introductionmentioning

confidence: 99%

A Sentiment Classification Model of E-Commerce User Comments Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machines

Jiang

2022

Scientific Programming

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“…It has been used in sentiment analysis and related fields such as opinion mining and emotion recognition. This resource was first created for English and due to its success it has been extended to many other languages as well [6,7,23,24]. This lexical resource assigns positivity, negativity and derived from the two, an objectivity score to each WordNet synset [22].…”

Section: Introductionmentioning

confidence: 99%

A Sentiwordnet Strategy for Curriculum Learning in Sentiment Analysis

Rao

Anuranjana

Mamidi

2020

Natural Language Processing and Information Systems

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Curriculum Learning (CL) is the idea that learning on a training set sequenced or ordered in a manner where samples range from easy to difficult, results in an increment in performance over otherwise random ordering. The idea parallels cognitive science's theory of how human brains learn, and that learning a difficult task can be made easier by phrasing it as a sequence of easy to difficult tasks. This idea has gained a lot of traction in machine learning and image processing for a while and recently in Natural Language Processing (NLP). In this paper, we apply the ideas of curriculum learning, driven by SentiWordNet in a sentiment analysis setting. In this setting, given a text segment, our aim is to extract its sentiment or polarity. SentiWordNet is a lexical resource with sentiment polarity annotations. By comparing performance with other curriculum strategies and with no curriculum, the effectiveness of the proposed strategy is presented. Convolutional, Recurrence and Attention based architectures are employed to assess this improvement. The models are evaluated on standard sentiment dataset, Stanford Sentiment Treebank.

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