Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques

Ruşeţi, Ştefan; Sirbu, Maria-Dorinela; Calin, Mihnea Andrei; Dascălu, Mihai; Trăușan-Matu, Ștefan; Militaru, Gheorghe

doi:10.1007/978-981-15-0637-6_27

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…As a final remark, we compare our results with the results obtained on the same dataset in [48] and in [40]. Our proposed Deep Neural Network architectures outperform with ∼10% the Transformer-based models in [40] that obtained an accuracy of only 0.67.…”

Section: Resultsmentioning

confidence: 75%

“…Furthermore, we observe that if the Topic Modeling algorithm does not meet these requirements, the polarity detection accuracy drops significantly, as in the case of LDA. Finally, we want to note that our proposed CNN-(BI)RNN architectures outperform the best performing state-of-the-art model with ∼10% applied on the same dataset in [40].…”

Section: Discussionmentioning

confidence: 97%

“…For the experiments, we used a game reviews dataset containing context textual data posted on the MetaCritic website (https://www.metacritic.com/, accessed on 28 September 2021). The original version of this dataset is presented in [39] and improved in [40]. From the dataset, we use only the reviews and the polarity assigned for each review, although the collected raw data also contain other information.…”

Section: Datasetmentioning

confidence: 99%

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Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

et al. 2021

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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.

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Section: Resultsmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 97%

Section: Datasetmentioning

confidence: 99%

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Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

et al. 2021

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“…Driven by the development of NLP, many sophisticated algorithms and models have been applied to sentiment analysis of game reviews to make it more intelligent and automated. Ruseti et al [10] used traditional support vector machine, multinomial Naive-Bayes, and deep neural networks (DNN) for triple classification of pre-processed game review texts. Secui et al [11] analyzed 9750 reviews using their own constructed model.…”

Section: Sentiment Analysis Of Game Reviewsmentioning

confidence: 99%

Sentiment analysis of Chinese game reviews based on SKEP

Wang¹

2023

AETR

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As China's game industry enters the era of the stock market, improving the quality of game products in all aspects is gradually becoming an important part of the development of China's game industry. There exists a large amount of game review data on the Internet, and sentiment analysis of them can provide important help for game makers to make decisions. However, the huge scale of game reviews and the extensive use of metaphors and sarcasm make it difficult to conduct sentiment analysis. In this paper, we experiment with a publicly available Chinese game review dataset using the ERNIE pre-training model equipped with the SKEP method, which improves the model's masking strategies and pre-training objectives to specialize the model's sentiment analysis ability. In addition, we compared the performance of ERNIE1.0, RoBERTa, MacBERT, BERT-wwm, and Nezha. The precision, recall, and F1 score of SKEP are higher than those of the models for comparison studies, which confirms the effectiveness of SKEP in this area.

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“…Sentiment analysis contains various processes such as subjectivity and polarity detection [4,77], emotion estimation [79], answering to the emotional questions [53], detecting spam comments [75], question answering [70], crime detection [38], sarcasm/irony detection [43], summarizing opinions [44] and many other subjects. Much research are currently conducting to extract user's comments from documents [15], sentences [57], or aspect-based [55]. Different methods of analyzing sentiments can be classified into three categories [73].…”

Section: Introductionmentioning

confidence: 99%

Deterministic solution of algebraic equations in sentiment analysis

Zahedi

Basiri

2023

Multimed Tools Appl

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Text mining methods usually use statistical information to solve text and language-independent procedures. Text mining methods such as polarity detection based on stochastic patterns and rules need many samples to train. On the other hand, deterministic and non-probabilistic methods are easy to solve and faster than other methods but are not efficient in NLP data. In this article, a fast and efficient deterministic method for solving the problems is proposed. In the proposed method firstly we transform text and labels into a set of equations. In the second step, a mathematical solution of ill-posed equations known as Tikhonov regularization was used as a deterministic and non-probabilistic way including additional assumptions, such as smoothness of solution to assign a weight that can reflect the semantic information of each sentimental word. We confirmed the efficiency of the proposed method in the SemEval-2013 competition, ESWC Database and Taboada database as three different cases. We observed improvement of our method over negative polarity due to our proposed mathematical step. Moreover, we demonstrated the effectiveness of our proposed method over the most common and traditional machine learning, stochastic and fuzzy methods.

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Comprehensive Exploration of Game Reviews Extraction and Opinion Mining Using NLP Techniques

Cited by 8 publications

References 14 publications

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

Sentiment analysis of Chinese game reviews based on SKEP

Deterministic solution of algebraic equations in sentiment analysis

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