Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment

Abdi, Asad; Shamsuddin, Siti Mariyam; Hasan, Shafaatunnur; Piran, Md. Jalil

doi:10.1016/j.eswa.2018.05.010

Cited by 55 publications

(15 citation statements)

References 49 publications

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“…Moreover, machine learning algorithms were also considered in the summarization process. In [ 44 ], Abdi et al presented a method for the summarization of opinion texts. This method is an extractive method that utilizes machine learning.…”

Section: Literature Review and Taxonomy Of Text Summarizationmentioning

confidence: 99%

Opinion texts summarization based on texts concepts with multi-objective pruning approach

2022

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Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure.

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Section: Literature Review and Taxonomy Of Text Summarizationmentioning

confidence: 99%

Opinion texts summarization based on texts concepts with multi-objective pruning approach

2022

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“…Jiang (2016a) also showed the superiority of incorporating four types of features: linguistic, sentiment, topic and word2vec features. Abdi et al (2018) combined multiple lexicons to build a high coverage lexicon, followed by the word2vec model to create a hybrid feature vector. In this paper, we attempt to eliminate the usage of expensive lexicons that may not comprise informal words.…”

Section: Related Workmentioning

confidence: 99%

Aspect context aware sentiment classification of online consumer reviews

Bansal

Srivastava

2020

IDD

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Purpose Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly determine the orientation of aspect related sentiment bearing word and overlook the aspect-context. Therefore, this paper aims to propose an aspect-context aware sentiment classification of OCR for deeper and more accurate insights. Design/methodology/approach In the proposed methodology, first, aspect descriptions and sentiment bearing words are extracted. Then, the skip-gram model is used to extract the first set of features to capture contextual information. For the second category of features, cosine similarity is used between a pre-defined seed word list and aspects, to capture aspect context sensitive sentiments. The third set of features includes weighted word vectors using term frequency-inverse document frequency. After concatenating features, ensemble classifier is used using three base classifiers. Findings Experimental results on two real-world data sets with variable lengths, acquired from Amazon.com and TripAdvisor.com, show that the advised ensemble approach significantly outperforms sentiment classification accuracy of state-of-the-art and baseline methods. Originality/value This method is capable of capturing the correct sentiment of ambiguous words and other special words by extracting aspect-context using word vector similarity instead of expensive lexical resources, and hence, shows superior performance in terms of accuracy as compared to other methods.

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“…The feature extraction is conducted for extracting the important vocabulary words from all the textual data and representing them in an appropriate format that is needed by the machine learning algorithms for further data analysis. Some of the common textual features are the Term Frequency-Inverse Document Frequency (TF-IDF) [11, 12], Bag-of-Words (BoW) [13], word n-grams [14], and the sentiment features [15]. In the past several years, authors have applied several Machine-learning and statistical theory-based techniques for classifying the texts.…”

Section: 0 Background and Related Workmentioning

confidence: 99%

Quality dimensions features for identifying high-quality user replies in text forum threads using classification methods

2019

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The Text Forum Threads (TFThs) contain a large amount of Initial-Posts Replies pairs (IPR pairs) which are related to information exchange and discussion amongst the forum users with similar interests. Generally, some user replies in the discussion thread are off-topic and irrelevant. Hence, the content is of different qualities. It is important to identify the quality of the IPR pairs in a discussion thread in order to extract relevant information and helpful replies because a higher frequency of irrelevant replies in the thread could take the discussion in a different direction and the genuine users would lose interest in this discussion thread. In this study, the authors have presented an approach for identifying the high-quality user replies to the Initial-Post and use some quality dimensions features for their extraction. Moreover, crowdsourcing platforms were used for judging the quality of the replies and classified them into high-quality, low-quality or non-quality replies to the Initial-Posts. Then, the high-quality IPR pairs were extracted and identified based on their quality, and they were ranked using three classifiers i.e., Support Vector Machine, Naïve Bayes, and the Decision Trees according to their quality dimensions of relevancy, author activeness, timeliness, ease-of-understanding, politeness, and amount-of-data. In conclusion, the experimental results for the TFThs showed that the proposed approach could improve the extraction of the quality replies and identify the quality features that can be used for the Text Forum Thread Summarization.

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Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment

Cited by 55 publications

References 49 publications

Opinion texts summarization based on texts concepts with multi-objective pruning approach

Opinion texts summarization based on texts concepts with multi-objective pruning approach

Aspect context aware sentiment classification of online consumer reviews

Quality dimensions features for identifying high-quality user replies in text forum threads using classification methods

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