W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis
Table 11: Impact of different seed words combination for the domain aspect classification.
['[BOLD] Aspects:{FOOD},{SERVICE},{AMBIENCE}', '[BOLD] Aspects acc.', '[BOLD] Polarity acc']
We use three different seed words for each domain aspect, in particular: food, chicken and burger for domain aspect FOOD; service, staff and waiter for domain aspect SERVICE; and ambience, atmosphere and décor for domain aspect AMBIENCE. We try different permutations and combinations of the seed words, including pairs of seed words for each domain aspect, and finally also the combination of the three seed words together. The results show that the accuracy is stable across all the combinations regardless of the chosen seed words. As expected, some combinations perform better than others but overall the average if high and the standard deviation is below 5% of the accuracy. The best result is obtained using all the seed words at the same time. This last fact is not surprising, since with more seeds the semantic coverage to guide the algorithm to the desired domain aspects is increased (as long as the seed words are semantically coherent with the domain aspect they are defining).