Abstract: The measure of individual investor sentiment in Chinese stock message board Guba Eastmony and its interactive relation to the market returns and trading volume is investigated. In order to measure sentiment, the construction of sentiment lexicon is a key procedure. Traditional methods for lexicon acquisition are commonly based on Semantic Orientation from Pointwise Mutual Information(SO-PMI) algorithm. A novel algorithm Semantic Orientation from Laplace Smoothed Normalized Pointwise Mutual Information(SO-LNPMI) is proposed, which has the higher accuracy for sentiment classification. Empirical analyses on the interactive relationship between individual investor sentiment and market returns and trading volume show that: (i) positive sentiment is the cause of market return while passive sentiment does not cause it; (ii) investor sentiment and trading volume present two-side Granger causality. In addition, an interesting phenomenon is that individual investors are enthusiastic about the use of emoticons when individual investors are positive.

Key words: investor sentiment, SO-LNPMI algorithm, Granger causality analysis