OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Keyvanpour, Mohammad Reza; Zandian, Zahra Karimi; Heidarypanah, Maryam

doi:10.1007/s13278-019-0622-6

Cited by 40 publications

(14 citation statements)

References 59 publications

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“…Zhang et al [ 56 ], Shakil et al [ 57 ] and Phu et al [ 58 ] used dictionary-based methods; Truică et al [ 59 ], Messina et al [ 60 ], Wang et al [ 61 ] and other studies used machine learning, whereas Szabóová et al [ 62 ], Ahmed et al [ 63 ] combine the dictionary-based methods and machine learning. The sentiment dictionary-based method is good at processing fine-grained text sentiment analysis, which is conducive to analyzing sentiment characteristics in specific fields [ 64 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Zeng

Tai

et al. 2022

Healthcare

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The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour.

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Section: Literature Reviewmentioning

confidence: 99%

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Zeng

Tai

et al. 2022

Healthcare

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“…In Keyvanpour et al [13], a useful technique was proposed based on lexicon and ML named OMLML with the help of social media networks. The major advantage of the presented technique, compared to another approach, is that the presented approach can simultaneously tackle the challenge.…”

Section: Related Workmentioning

confidence: 99%

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

Eshmawi¹,

Alhumyani²,

Khalek³

et al. 2022

Computers, Materials &Amp; Continua

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Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recent years. Likewise, Machine Learning (ML) approaches is one of the interesting research domains that are highly helpful and are increasingly applied in several business domains. In this background, the current research paper focuses on the design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviated as DHOA-FNN model. The proposed DHOA-FNN technique involves four different stages namely, preprocessing, feature extraction, classification, and parameter tuning. In addition to the above, the proposed DHOA-FNN model has two stages of feature extraction namely, Glove and N-gram approach. Moreover, FNN model is utilized as a classification model whereas GTOA is used for the optimization of parameters. The novelty of current work is that the GTOA is designed to tune the parameters of FNN model. An extensive range of simulations was carried out on the benchmark dataset and the results were examined under diverse measures. The experimental results highlighted the promising performance of DHOA-FNN model over recent state-of-the-art techniques with a maximum accuracy of 0.9928.

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“…Several works tackle the problem of OM as a special case of ATC 64 . In general, OM problems may be performed with different approaches: lexicon‐based, text categorization techniques, and hybrid approaches 65 . They are challenged by ambiguity, the presence of sarcasm or irony in the text, objective and subjective views, and so forth.…”

Section: Optimization and Limitationsmentioning

confidence: 99%

Supervised classification by thresholds: Application to automated text categorization and opinion mining

Cherif¹,

Madani

Kissi³

2021

Concurrency and Computation

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Over recent years, the world has experienced explosive growth in the volume of textual data, which makes a manual analysis impossible. Machine learning techniques provided an effective solution to this problem. Due to its capacity to organize the huge and varied amounts of data, it offered valuable insights and it has become an emerging investigative field for the research community. Classification techniques are used to classify data into different classes according to desired criteria. By their simplicity, they give rise to a variety of applications: automated text categorization, opinion mining, and so forth. These processes go through three stages: text representation, features extraction, and the classification process; they still face many difficulties due both to the complex nature of text databases and to the high dimensionality of texts representations. This article presents a new classification approach that learns to classify texts from the most reliable features more accurately. The added advantage of the proposed approach is that it automatically classifies a text without necessarily processing all its features. The experimental results showed that this new classification by thresholds outperforms the state‐of‐the‐art methods. As a result, the obtained f‐measure on automatic text categorization was 95.06% while it is lower on opinion mining.

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OMLML: a helpful opinion mining method based on lexicon and machine learning in social networks

Cited by 40 publications

References 59 publications

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

Supervised classification by thresholds: Application to automated text categorization and opinion mining

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