Sentiment analysis of Chinese online reviews using ensemble learning framework

Huang, Jiafeng; Xue, Yun; Hu, Xiaohui; Jin, Huixia; Lü, Xin; Liu, Zhihuang

doi:10.1007/s10586-018-1858-z

Cited by 11 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study shows that by combining the predictions of multiple models, ensemble methods can achieve more accurate and reliable sentiment analysis results in various applications, such as product review analysis, social media monitoring, and customer feedback analysis. Numerous studies in the domain have demonstrated that by combining the predictions of multiple classifiers, ensemble methods can reduce the risk of overfitting, improve accuracy and robustness, and provide a more balanced view of outputs [22,29].…”

Section: Discussionmentioning

confidence: 99%

“…Ensemble techniques also provide a flexible framework for incorporating new data and updating the model over time. As new data becomes available, ensemble methods can adapt and re-weight the base classifiers to better capture the changing sentiment trends and dynamics in the data [29,30]. This adaptability is particularly useful in sentiment analysis applications that involve analyzing social media streams, where the sentiment can change rapidly in response to events and trends.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis

Shah

Ghomeshi

Vakaj

et al. 2023

BDCC

View full text Add to dashboard Cite

Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis

Shah

Ghomeshi

Vakaj

et al. 2023

BDCC

View full text Add to dashboard Cite

show abstract

“…For the Chinese sentiment analysis research, Peng et al [51] discussed sentiment classification methods for the Chinese language, such as machine learning-based approaches, knowledge-based approaches and mixed models, and tested these approaches with various Chinese datasets. Huang et al [52] proposed a new ensemble learning framework for the sentiment classification of Chinese online reviews. By using the random subspace classifier based on product attributes, the sentiment information of online reviews could be obtained.…”

Section: B Approaches To Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis of Danmaku Videos Based on Naïve Bayes and Sentiment Dictionary

Jin

2020

IEEE Access

View full text Add to dashboard Cite

Danmaku video provides a platform for users to communicate online while watching videos. Danmaku is a live commenting function where the comments related to the video being screened are created by users and prominently shown in real-time on the video screen. These live comments contain complex and rich sentiments, reflecting users' instant opinions and feelings on video programs. In some sense, danmaku provides emotional timing information about video data, and it also offers an innovative mean to analyze video data. However, existing sentiment classification methods are not suitable for danmaku data analysis. To solve this problem, this paper constructs a danmaku sentiment dictionary and presents a new method using sentiment dictionary and Naïve Bayes for the sentiment analysis of danmaku reviews. The method is greatly helpful in supervising the overall emotional orientation of a danmaku video and predicting its popularity. Through the processes of extracting emotional information from a danmaku video, classifying sentiment and visualizing data, the time distribution of the seven sentiment dimensions can be obtained. In addition, a weight calculation can be conducted for classifying the sentiment polarity of danmaku reviews. Experimental results show that the proposed method has a significant effect on sentiment score and polarity detection.

show abstract