As internet technology develops, it has changed the way people search information and share user-generated content, as well as the way people communicate with each other. Hence, people are now engaged in a new form of word of mouth, Electronic Word of Mouth (eWOM). An increasing number of people are consulting online reviews in their decision-making processes. Google named this moment of searching, Zero Moment of Truth (ZMOT). According to the company (2011), 88% of consumers gather information from others’ experiences before the actual moment of purchase. Since today searching information is accessible from anywhere—on any device and at any given time, ZMOT is part of everyday life.
The growth of eWOM, as well as the availability of huge data coincide with those of studies in opinion mining (or also called as sentiment analysis). Majority studies in the field have focused on extracting positive and negative opinions expressed in online reviews and the target of these opinions. However, my research goes beyond the coarse-grained positive vs. negative opposition and propose a corpus-based model that detects evaluative language at a finer-grained level. With this model, we classify sentences into one of four evaluation types: opinion, suggestion, intention and description.
Moreover, previous works assume that positive and negative classes are evenly distributed. However, real time applications show that evaluation categories are highly imbalanced (Gopalakrishnan & Ramaswamy, 2014). This is confirmed in my data: observations per evaluation type are unevenly distributed (positive opinions account for 68% of all evaluations). To deal with the uneven distribution of evaluations, I use resampling and algorithmic approaches. The methodology for the classification problem will be detailed in the following posts.
Throughout the following posts, I will also consider questions such as: How do people express and evaluate their experience? What are some of the common linguistic features used in online reviews? What are some discourse features used in positive versus negative reviews? How the valence can be modified from one pole to the other? What are the topics talked about? How do evaluation change in time?
By introducing my research, I hope to provide a more comprehensive picture of the full spectrum of evaluations in opinion mining and sentiment analysis.
References
Dichter, Ernest. (1966). How word-of-mouth advertising works. Harvard Business Review, 44(6), 147-166.
Gopalakrishnan, Vinodhini., and Chandrasekaran Ramaswamy. (2014). Sentiment Learning from Imbalanced Dataset: An Ensemble Based Method, International Journal of Artificial Intelligence, vol. 12, no. 2, pp. 75-87.
Google and Shopper Sciences. (2011). “The Zero Moment of Truth Macro Study”, in Think With Google, 09/10/2018, https://www.thinkwithgoogle.com/consumer-insights/the-zero-moment-of-truth-macro-study/.
