Research On The Impact Of Sentiment Expression On Consumer Perception Of Online Reviews Helpfulness

Online reviews are user-generated product information posted on retailer or third partywebsites. Including previous consumers’ subjective sentiment information such as productevaluation and user experience, online reviews are consider to be more credible and infectious,and become a complement to product information from suppliers. Previous studies indicated thatsentiment expression of online reviews has direct impact on consumer product attitude and purchasebehavior. Because online reviews from most websites are written in free-text format with very scantstructured metadata information and the volume of online reviews are very large. Most of previousstudies use online ratings instead of online reviews’ sentiment orientation. In recent years, text miningtechnologies facilitate the sentiment information extraction and sentiment orientation analysis of onlinereviews. Research on the impact of sentiment expression on consumer perception and behavior throughtext mining technologies can help the company to understand consumer behavior and make marketingstrategies more effectively.Using online reviews and helpfulness votes as the main research data, this paper investigatesthe impact of sentiment expression on consumer perception of reviews helpfulness. Our aim is tohelp the company to optimize the online reviews system and make marketing strategies moreeffectively. This pape first investigates the effects of information structure factors of onlinereviews including review number, review length, review direction and review type on consumerperception and behavior. The results of this research serve as theoretical framework. Then thispaper focuses on the impact of sentiment expression on consumer perceived reviews helpfulness.Specifically, this involves three parts: methods for sentiment information extraction and sentimentorientation analysis; the impact of product feature words and sentiment words on consumerperceived reviews helpfulness and the impact of the sentiment orientation including direction,intensity and admixture on consumer perceived reviews helpfulness. The detailed researches are asfollows:(1) The influence of information structure on consumer perception and behavior. This paperdefines four dimensions of information structure and proposes the model to describe the influenceof information structure factors of online reviews on consumer perception and behavior. In thismodel, the differential effects of information structure factors are analyzed and the paths ofinfluence are also revealed. We conduct a2×2×2×2between-subjects factorial design experimentto validate the hypotheses. The results show the effects of the number, the length and the type ofonline reviews on the perceived reviews helpfulness, the effects of the number and the type ofonline reviews on the perceived reviews credibility, the effect of the number of online reviews onthe perceived product popularity as well as the effects of the perceived reviews helpfulness, theperceived reviews credibility and the perceived product popularity on purchase intension. Theresults and implications of this research are discussed.(2) Methods for sentiment information extraction and sentiment orientation analysis. Thispaper investigates the method for extracting product feature words and sentiment words from online reviews, the method for the paradigm words selection with intensity information and wordsentiment orientation discrimination and the method for sentiment orientation combination basedon product features relationship recognition. This paper adopts the statistic-based method toextract the words. The method is proved to be effective in the experiment. This paper proposes amethod for the paradigm words selection with intensity information and word sentimentorientation discrimination. With this method, we can distinguish the direction and intensity ofsentiment words as well as differentiate the sentiment orientation of the words whose orientatindepending on the context. This paper proposes a method for sentiment orientation combinationbased on product features relationship recognition. The method can be used in sentiment analysisfor the reviews covering several product feature words, especially for the reviews of complex andmulti-function product.(3) The impact of product feature words and sentiment words on consumer perceivedreviews helpfulness. Based on theories of product quality perception, this paper dichotomizeproduct feature words into intrinsic attribute words and extrinsic attribute words and dichotomizesentiment words into evaluation words and emotion words. This paper investigate the impact ofintrinsic attribute words, extrinsic attribute words, evaluation words and emotion words onconsumer perception of reviews helpfulness. Mobile and music CD are selected as therepresentation of search product and experience product. Product feature words and sentimentwords are extracted and classified through text mining methods. The effects of product featurewords and sentiment words on consumer perception of reviews helpfulness are tested using thehelpfulness voting data. The study shows that intrinsic attribute words and evaluation words havemore effects on perceived helpfulness for search product while extrinsic attribute words andemotion words have more effects on perceived helpfulness for experience product.(4) The impact of the sentiment orientation on consumer perceived reviews helpfulness.This paper divides helpfulness voting process into two stages and then investigates the differentialeffects of sentiment orientation including direction, intensity and admixture during these twostages. This paper extracts sentiment words from online reviews and calculates sentimentorientation of the word, the context and the whole text using text mining methods. The effects ofsentiment orientation on total votes and helpfulness are tested through statistic model andparameters estimate. The results from mobile reviews dataset show that the reviews with higherintensity attract more attention and then get a large number of total votes; the reviews with higheradmixture are perceived to be more helpful; negative reviews are perceived to be more helpful.The results of this paper can help online sellers to optimize the online reviews system and makemarketing strategies more effectively.