Aspect Based Sentiment Analysis: What the heck is this?
Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of existing evaluations in Sentiment Analysis (SA) are aimed at evaluating the overall polarity of a sentence, paragraph, or text span. In contrast, this task will provide evaluation for detecting aspects and the sentiment expressed towards each aspect.
The task of Aspect Based Sentiment Analysis constitutes of:
Sub task 1: Aspect term extraction
Given a set of sentences with pre-identified entities (e.g., restaurants), we identified the aspect terms present in the sentences.An aspect term names a particular aspect of the target entity (e.g., "I liked the service and the staff, but not the food”, “The food was nothing much, but I loved the staff”).
Sub task 2: Aspect term polarity:
? Given one or more aspect terms within a sentence, determine whether the polarity of each aspect term is positive, negative, neutral or conflict (i.e., both positive and negative).
For example:
“I loved their fajitas” → {fajitas: positive}
“I hated their fajitas, but their salads were great” → {fajitas: negative, salads: positive}
“The fajitas are their first plate” → {fajitas: neutral}
“The fajitas were great to taste, but not to see” → {fajitas: conflict}
Algorithm: Recurrent Attention Network on Memory (Deep Learning)
Sub task 3: Aspect category detection
Given a predefined set of aspect categories (e.g., price, food), identify the aspect categories discussed in a given sentence. Aspect categories are typically coarser than the aspect terms of Sub task 1, and they do not necessarily occur as terms in the given sentence. For example, given the set of aspect categories {food, service, price, ambiance, anecdotes/miscellaneous}:
“The restaurant was too expensive” → {price}
“The restaurant was expensive, but the menu was great” → {price, food}
Sub task 4: Aspect category polarity
Given a set of pre-identified aspect categories (e.g., {food, price}), determine the polarity (positive, negative, neutral or conflict) of each aspect category. For example:
“The restaurant was too expensive” → {price: negative}
“The restaurant was expensive, but the menu was great” → {price: negative, food: positive}