The Web has changed the way that consumers express their opinions. They can now post 
reviews of products and express their opinions on almost anything on the websites. 
Potential customers often search online for product information and they often have 
access to hundreds of product reviews from other customers. Some of the reviews are 
found to be more helpful than other reviews as evidenced the potential customer’s 
helpful ness vote. This online word
- of - mouth (WOM) behavior represents new and 
measurable sources of information. 
Recent research has shown that helpfulness votes of customer reviews can have a positive 
influence on sales. While it is clear that helpfulness vote 
of a review is important, less is 
known about why certain pieces of online review are more helpful than others. Despite 
the fact that, customers encounter a variety of emotions in a purchase situation and those 
emotions are likely to be documented in the review, few researches have investigated 
how emotions elicited by the review affect the helpfulness of the review
beyond the 
valence. Do discrete emotions have differential informational value in this case? Based on 
cognitive appraisal theory, in the first 
essay of this dissertation, I examine how specific 
emotions (hope, happiness, anxiety, disgust etc.) embedded in the review affect the 
helpfulness votes of potential customer. I adopt 
a quantitative content analysis (Latent Semantic Analysis) 
approach to measure emotions in these reviews. 
In the second essay of the dissertation, 
I explore how the
topics of online reviews differ 
between
positive and negative reviews. 
Examination of real product reviews shows that 
there are thematic differences 
between 
them. 
Also, service related complains are found to 
be more helpful by potential customers. 
This enables us to better understand the 
conceptual differences in WOM .
Lastly, in the third essay, I compare two text mining techniques: Latent Semantic 
Analysis and Probabilistic Latent Semantic Analysis (PLSA) in extracting common 
themes in the positive and negativ
e product reviews. Results shows that the choice of text 
mining approaches should be based on the goal of the marketing researcher. If the goal is 
to learn about a specific brand, PLSA might reveal more specific information. However, 
if the goal is to learn about important aspects of a broader product category, LSA works 
better in terms of interpretability.