AspeRa: Aspect-based Rating Prediction Model
Figure 2: Comparing AspeRa with GloVe (SGNS clusters), ABAE (SGNS clusters), and LDA with the same vocabulary and 10 topics on Instant Videos; more is better. X-axis: number of top-ranked representative words per aspect, Y-axis: topic coherence scores.
['[BOLD] Model', '[BOLD] MSE  [BOLD] Instant Videos', '[BOLD] MSE  [BOLD] Toys & Games']
We evaluated the proposed model on Amazon Instant Videos 5-core reviews and Amazon Toys and Games 5-core reviews The first dataset consists of reviews written by users with at least five reviews on Amazon and/or for items with at least five reviews; it contains 37,126 reviews, 5,130 users, 1,685 items, and a total of 3,454,453 non-unique tokens. The second dataset follows 5 minimum reviews rule; it contains 167,597 reviews, 19,412 users, 11,924 items, and a total of 17,082,324 non-unique tokens. We randomly split each dataset into 10% test set and 90% training set, with 10% of the training set used as a validation set for tuning hyperparameters. Since this model utilizes an aspect embedding matrix to approximate aspect words in the vocabulary, initialization of aspect embeddings is crucial. We compare two word embeddings for AspeRa: We adopted a GloVe model trained on the Wikipedia 2014 + Gigaword 5 dataset (6B tokens, 400K words vocabulary, uncased tokens) with dimension 50. We compared the performance of AspeRa with OnlineLDA Qualitative analysis shows that some aspects describe what could be called a topic (a set of words diverse by part of speech and function describing a certain domain), some encode sentiment (top words are adjectives showing attitude to certain objects discussed in the text), and some encode names (actors, directors, etc.). Thus, most aspects are clearly coherent, but there is room for improvement.