I have uploaded the pre-trained word2vec to obtain the word vectors for restaurant ... To use the pre-trained model, download the code and start working.
Assignment 3: SemEval-2016 Task 5 Aspect Category Detection in Aspect-Based Sentiment Analysis Marks: 100 Due Date:
Description: Given a set of user reviews about a target entity (e.g. a Laptop, a Restaurant or a Hotel), the goal is to identify all the aspect categories. Aspect categories are usually predefined.
Example review sentences S1: I was very disappointed with this restaurant. S2: I’ve asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away. S3: Chow fun was dry; pork shu mai was more than usually greasy and had to share a table with loud and rude family.
In this task we have twelve predefined aspect categories for the given training set, as shown below
Training and Test sets: Download the training data from the following links. In these files, tab is used as a separator between classes and a review sentence.
Training set Test set
Pre-trained Word2vec model for this task: I have uploaded the pre-trained word2vec to obtain the word vectors for restaurant training sentences. The dimensions of words for the given model is [400x1].
Trained word vector model To use the pre-trained model, download the code and start working.
If you are interested in training your own wprd2vec model:
Word vector tutorial is quick start for you to learn. Also read word2vec paper to build you initial understanding.
Evaluation: Evaluation will be done on test set. Use F1-microaveraging method to compare given and predicted aspect categories.
Previous scores in this task:
Aspect category detection systems 1. 2. 3. 4. 5. 6.
Noman ACD model NLANGP NileT BUTkn AUEB SYSU
F1 Scores 76.06 73.031 72.886 72.396 71.537 70.869
Some Previous techniques: 1. NLANGP Systems for Aspect Category Detection System 2. Representational learning for aspect based sentiment analysis
