基于情感分类的竞争企业新闻文本主题挖掘

Mining News on Competitors with Sentiment Classification

【目的】在竞争情报分析中,改进新闻报道信息主题识别效率,降低情报搜集成本,提升分析的即时性。【应用背景】适用于企业竞争情报人员通过新闻媒体对企业自身和竞争对手的报道抓取和主题识别,及时感知重要动态。【方法】使用情感分析API对爬取的新闻报道数据做出分类,利用LDA识别主题,并进行可视化分析。采用Python完成数据采集、清洗、分析与可视化等流程。【结果】从共享单车新闻中,识别出正负面情绪的不同主题,并且找出对应的主要特征词汇。【结论】基于情感分类的主题挖掘方法有助于企业聚焦自身与竞争对手的主要优势与问题,可以改进环境扫描与竞争情报的时效性和准确性。

[Objective] This paper aims to improve the efficiency of topic modeling from news reports, and reduce the cost of competitive intelligence analysis. [Context] The proposed method could help competitive intelligence analysts accomplish environmental scanning tasks with the help of news reports. [Methods] First, we retrieved news stories with the help of a web crawler. Then, we categorized these articles based on a sentiment analysis API. Third, we identified and visualized news topics with the help of Latent Dirichlet Allocation method. We used Python to finish the data collection, cleansing, analyzing and visualizing jobs. [Results] We identified positive and negative sentiments as well as related keywords from news reports on the bike-sharing industry. [Conclusions] The proposed topic mining method based on sentiment analysis helps enterprises identify competitive advantages. It also improves the effectiveness of environmental scanning for competitive intelligence.