Opinion Score Mining: An Algorithmic Approach

Bhatia, Surbhi; Шарма, Маниша; Bhatia, Komal Kumar

doi:10.5815/ijisa.2017.11.05

Cited by 5 publications

(2 citation statements)

References 15 publications

(14 reference statements)

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“…A mouse-equipped framework for the perception of handwritten letters or images was proposed [18]. [19,20] presented a comparison of opinion summarization techniques and proposed an unsupervised algorithmic approach for opinion summarization that does not require annotated training data, adequately classifies the input text and achieves 96% accuracy. [21] classify the raw text using the danger theory of the Artificial Immune System (AIS).…”

Section: Literature Reviewmentioning

confidence: 99%

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Dhankhar¹,

Gupta²,

Memon³

et al. 2022

Computer Systems Science and Engineering

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In today's digital era, the text may be in form of images. This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM). A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language. A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation. SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position, numerical data, inverted comma, sentence length, and keywords features. The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features. The second goal is to maintain or improve the classification system's performance. The experiment included news articles from various genres, such as Bollywood, politics, and sports. The proposed method's accuracy for Hindi character recognition is 96.97%, which is good compared with baseline approaches, and system-generated summaries are compared to human summaries. The evaluated results show a precision of 72% at a compression ratio of 50% and a precision of 60% at a compression ratio of 25%, in comparison to state-of-the-art methods, this is a decent result.

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Section: Literature Reviewmentioning

confidence: 99%

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Dhankhar¹,

Gupta²,

Memon³

et al. 2022

Computer Systems Science and Engineering

Self Cite

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“…Purity and entropy can also be considered as performance metrics [39]. The number of overlapping sentences/no of sentences in system generated summary is given by p. The number of overlapping sentences/no of sentences in gold summary denoted r. The harmonic mean of the above two is denoted as f mathematically [40].…”

Section: Great Taffymentioning

confidence: 99%

An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining

Bhatia¹,

Alojail²

2022

Computer Systems Science and Engineering

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Opinion summarization recapitulates the opinions about a common topic automatically. The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text. The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining. This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using seq2seq model. It presents a paradigm shift with regard to the way extractive summaries are generated. Novel algorithms for word extraction using assertions are proposed. The semantic framework is well-grounded in this research facilitating the correct decision making process after reviewing huge amount of online reviews, considering all its important features into account. The advantages of the proposed solution provides greater computational efficiency, better inferences from social media, data understanding, robustness and handling sparse data. Experiments on the different datasets also outperforms the previous researches and the accuracy is claimed to achieve more than the baselines, showing the efficiency and the novelty in the research paper. The comparisons are done by calculating accuracy with different baselines using Rouge tool.

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