Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (2): 87-95    DOI: 10.11925/infotech.2096-3467.2017.02.12
Orginal Article Current Issue | Archive | Adv Search |
A Sentiment Analysis Model Based on Temporal Characteristics of Travel Blogs
Cheng Cuiqiong, Xu Jian()
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
Download: PDF (900 KB)   HTML ( 28
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study aims to find the temporal-distribution patterns of tourists’ attitudes towards their destinations through sentiment analysis of travel blogs. [Context] More and more tourists collect information on their destinations from travel blogs, which provide enormous business opportunities. [Methods] We proposed a sentiment analysis model based on temporal characteristics of travel blogs. It includes the following modules: data collection, preprocessing, identifying sentiment words, weight calculation, and analysis. The model was examined with four types of travel blogs. [Results] The number of post with “good” emotion was always higher than others each month. The volatility of “good”, “happiness” and “disgust” emotion was the highest in different months. The volatility emotion over time was not correlated to the number of related travel blogs. There is no relationship between the peak/off seasons and the emotion of tourists. [Conclusions] The proposed model could identify the changing of tourist sentiment over time, which provides new information for tourism managers and potential visitors.

Key wordsTravel Blogs      Sentiment Analysis      Sentiment Lexicon      Temporal Characteristics     
Received: 07 October 2016      Published: 27 March 2017
ZTFLH:  G350  

Cite this article:

Cheng Cuiqiong,Xu Jian. A Sentiment Analysis Model Based on Temporal Characteristics of Travel Blogs. Data Analysis and Knowledge Discovery, 2017, 1(2): 87-95.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.02.12     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I2/87

[1] 杨立公, 朱俭, 汤世平. 文本情感分析综述[J]. 计算机应用, 2013, 33(6): 1574-1578.
doi: 10.3724/SP.J.1087.2013.01574
[1] (Yang Ligong, Zhu Jian, Tang Shiping.Survey of Text Sentiment Analysis[J]. Journal of Computer Applications, 2013, 33(6): 1574-1578.)
doi: 10.3724/SP.J.1087.2013.01574
[2] Hu M, Liu B.Mining and Summarizing Customer Reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2004.
[3] 朱嫣岚, 闵锦, 周雅倩, 等. 基于HowNet的词汇语义倾向计算[J]. 中文信息学报, 2006, 20(1): 14-20.
doi: 10.3969/j.issn.1003-0077.2006.01.003
[3] (Zhu Yanlan, Min Jin, Zhou Yaqian, et al.Semantic Orientation Computing Based on HowNet[J]. Journal of Chinese Information Processing, 2006, 20(1): 14-20.)
doi: 10.3969/j.issn.1003-0077.2006.01.003
[4] 史伟, 王洪伟, 何绍义. 基于微博平台的公众情感分析[J]. 情报学报, 2012, 31(11): 1171-1178.
doi: 10.3772/j.issn.1000-0135.2012.11.007
[4] (Shi Wei, Wang Hongwei, He Shaoyi.Study on Public Sentiment Based on Microblogging Platform[J]. Journal of the China Society for Scientific and Technical Information, 2012, 31(11): 1171-1178.)
doi: 10.3772/j.issn.1000-0135.2012.11.007
[5] Pang B, Lee L, Vaithyanathan S.Thumbs up?: Sentiment Classification Using Machine Learning Techniques[C]// Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. 2002.
[6] 徐军, 丁宇新, 王晓龙. 使用机器学习方法进行新闻的情感自动分类[J]. 中文信息学报, 2007, 21(6): 95-100.
doi: 10.3969/j.issn.1003-0077.2007.06.013
[6] (Xu Jun, Ding Yuxin, Wang Xiaolong.Sentiment Classification for Chinese News Using Machine Learning Methods[J]. Journal of Chinese Information Processing, 2007, 21(6): 95-100.)
doi: 10.3969/j.issn.1003-0077.2007.06.013
[7] Banić L, Mihanović A, Brakus M.Using Big Data and Sentiment Analysis in Product Evaluation[C]// Proceedings of the 36th International Convention on Information & Communication Technology Electronics & Microelectronics(MIPRO). 2013.
[8] 王素格, 吴苏红. 基于依存关系的旅游景点评论的特征-观点对抽取[J]. 中文信息学报, 2012, 26(3): 116-121.
doi: 10.3969/j.issn.1003-0077.2012.03.020
[8] (Wang Suge, Wu Suhong.Feature-Opinion Extraction in Scenic Spots Reviews Based on Dependency Relation[J]. Journal of Chinese Information Processing, 2012, 26(3): 116-121.)
doi: 10.3969/j.issn.1003-0077.2012.03.020
[9] 郑文英. 旅行目的地中文评论的情感分析研究[D]. 哈尔滨: 哈尔滨工业大学, 2010.
[9] (Zheng Wenying.Sentiment Analysis of Travel Destination Reviews in Chinese[D]. Harbin: Harbin Institute of Technology, 2010.)
[10] 金程. 游客情感的动态性及其变化机制研究[D]. 广州: 华南理工大学, 2015.
[10] (Jin Cheng.Research on the Dynamics and Change Mechanism of Tourist Emotions [D]. Guangzhou: South China University of Technology, 2015.)
[11] 胡传东, 李露苗, 罗尚焜. 基于网络游记内容分析的风景道骑行体验研究——以318国道川藏线为例[J]. 旅游学刊, 2015, 30(11): 99-110.
doi: 10.3969/j.issn.1002-5006.2015.11.014
[11] (Hu Chuandong, Li Lumiao, Luo Shangkun.Cycling Tourists’ Experience of Scenic Byways Based on Content Analysis of Travel Blogs: A Case Study of the Sichuan-Tibet Section of National Highway 318[J]. Tourism Tribune, 2015, 30(11): 99-110.)
doi: 10.3969/j.issn.1002-5006.2015.11.014
[12] 于静. 基于微博大数据的游客情感及时空变化研究[D]. 西安: 陕西师范大学, 2015.
[12] (Yu Jing.Research on Tourist Emotion and Spatio-temporal Variation Based on Microblog Big Data[D]. Xi’an: Shaanxi Normal University, 2015.)
[13] Li Q, Wu Y, Wang S, et al.VisTravel: Visualizing Tourism Network Opinion from the User Generated Content[J]. Journal of Visualization, 2016, 19(3): 1-14.
doi: 10.1007/s12650-015-0330-x
[14] 火车采集器[CP/OL]. [2016-11-15]. .
[14] (LocoySpider [CP/OL]. [2016-11-15].
[15] 蚂蜂窝[DB/OL]. [2016-11-15]. .
[15] (mafengwo.com [DB/OL]. [2016-11-15].
[16] 贺涛. 面向中文博客的信息采集与倾向性检索[D]. 合肥: 中国科学技术大学, 2009.
[16] (He Tao.Research on Chinese Blog Information Gathering and Opinion Retrieval [D]. Hefei: University of Science and Technology of China, 2009.)
[17] 杜振雷. 面向微博短文本的情感分析研究[D]. 北京: 北京信息科技大学, 2013.
[17] (Du Zhenlei.Sentiment Analysis Towards Microblog Short Text [D]. Beijing: Beijing Information Science and Technology University, 2013.)
[18] Standard Deviation [EB/OL]. [2016-11-15]..
[1] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[2] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[3] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[4] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[5] Li Feifei,Wu Fan,Wang Zhongqing. Sentiment Analysis with Reviewer Types and Generative Adversarial Network[J]. 数据分析与知识发现, 2021, 5(4): 72-79.
[6] Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method[J]. 数据分析与知识发现, 2021, 5(3): 121-131.
[7] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[8] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[9] Lv Huakui,Liu Zhenghao,Qian Yuxing,Hong Xudong. Relationship Between Financial News and Stock Market Fluctuations[J]. 数据分析与知识发现, 2021, 5(1): 99-111.
[10] Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(7): 110-117.
[11] Jiang Lin,Zhang Qilin. Research on Academic Evaluation Based on Fine-Grain Citation Sentimental Quantification[J]. 数据分析与知识发现, 2020, 4(6): 129-138.
[12] Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing[J]. 数据分析与知识发现, 2020, 4(5): 1-14.
[13] Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
[14] Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
[15] Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn