Analysis of Topic and Sentiment Trends in Customer Reviews Before and After Covid-19 Pandemic

Suryadi, Dedy; Fransiscus, Hanky; Chandra, Yoko Gunawan

doi:10.1109/ivit55443.2022.10033397

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…According to [18] their work analyzes customer reviews to identify topics and trends, as hot topic trends typically refer to current popular or frequently discussed topics. The two concepts are related, this paper specifically focuses on customer reviews and LDA topic modeling, whereas we utilize word2vec models.…”

Section: A Machine Learning-based Approachesmentioning

confidence: 99%

“…By comparing and contrasting the proposed method with state-of-the-art approaches, it is possible to evaluate its effectiveness and highlight its contribution to the field. [18], Behpour [19], Lv [20], Cavalli [23], Ravi [25], Azizi [21], and Alkhodair [37] have researched trend detection using different methods. However, none of their studies included the distributed representations method, stream text data, chronological analysis of the data, visualization models for topic trends, or distributed representations-based knowledge graphs.…”

Section: G Comparison To State-of-the-art Approachesmentioning

confidence: 99%

“…While these approaches focus on trends within a particular field, our methodology is designed to detect trends across different fields, including both technical and general areas. Furthermore, Suryadi [18] and Azizi [21] proposed frameworks limited to specific research areas and data domains and lacking future applicability beyond the COVID-19 pandemic. In contrast, the proposed study goes beyond previous research by utilizing chronological analysis, developing a word2vec-based visualization model, and creating a distributed representations-based knowledge graph.…”

Section: G Comparison To State-of-the-art Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations

Khan,

Xia,

Ali

et al. 2023

IEEE Access

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Hot topic trends have become increasingly important in the era of social media, as these trends can spread rapidly through online platforms and significantly impact public discourse and behavior. As a result, the scope of distributed representations has expanded in machine learning and natural language processing. As these approaches can be used to effectively identify and analyze hot topic trends in large datasets. However, previous research has shown that analyzing sequential periods in data streams to detect hot topic trends can be challenging, particularly when dealing with large datasets. Moreover, existing methods often fail to accurately capture the semantic relationships between words over different time periods, limiting their effectiveness in trend prediction and relationship analysis. This paper aims to utilize a distributed representations approach to detect hot topic trends in streaming text data. For this purpose, we build a sequential evolution model for a streaming news website to identify hot topic trends in streaming text data. Additionally, we create a visual display model and knowledge graph to further enhance our proposed approach. To achieve this, we begin by collecting streaming news data from the web and dividing it chronologically into several datasets. In addition, word2vec models are built in different periods for each dataset. Finally, we compare the relationship of any target word in sequential word2vec models and analyze its evolutionary process. Experimental results show that the proposed method can detect hot topic trends and provide a graphical representation of any raw data that cannot be easily designed using traditional methods.

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Section: A Machine Learning-based Approachesmentioning

confidence: 99%