This study explores the effects of textual tone in analyst reports on the stock price synchronicity. Based on 377 644 sell-side analyst reports of Chinese listed firms from 2006 to 2018，10 434 sentences from analyst reports are randomly selected and these training materials are manually classified into positive，neutral and negative categories. To compare superiority of different methods，11 machine learning algorithms are applied to train the labeled sentences，and Nave Bayes is finally used to measure the analyst report tone. The empirical results show that the tone of analyst report is negatively associated with the stock price synchronicity of the companies. Although the results are different from most of the conclusions of the existing researches，the results can be well explained by the individual selective perception theory in the capital market of China， where the short-selling mechanism is underdeveloped. Furthermore，the analyst’s positive textual tone improves the information efficiency of the stock market by 1) stimulating the firms to issue more announcements， 2) guiding institutional investors to buy and 3) attracting other analysts to release more reports. This study has important implications for investors to focus on the index of textual tone，for listed companies to strengthen the information disclosure management，and for government departments to improve the capital market system.