Clustering text data based on sentiment?

Question

I am scraping reviews off Amazon with the intent to perform sentiment analysis to classify them into positve, negative and neutral. Now the data I would get would be text and unlabeled.

My approach to this problem would be as following:-

1.) Label the data using clustering algorithms like DBScan, HDBScan or KMeans. The number of clusters would obviously be 3.

2.) Train a Classification algorithm on the labelled data.

Now I have never performed clustering on text data but I am familiar with the basics of clustering. So my question is:

1. Is my approach correct?

2. Any articles/blogs/tutorials I can follow for text based clustering since I am kinda new to this?

PS: I am familiar with both NLP and Clustering algo's but I have never performed Clustering on text data.

Answer 1

In my opinion there are two main problems with your approach:

• The clustering is extremely unlikely to correspond to sentiment, unless the features that you use for clustering are specifically engineered to represent sentiment. In general text clustering tend to group documents by common words, i.e. similar topic. This might lead to different categories of reviews by type of product, for example.
• The second and I think most important issue is that without any labelled data, you can't evaluate the system. A common mistake would be to use the classes obtained from the clustering in order to evaluate the classification model: this doesn't evaluate the full task of sentiment analysis since there's no way to know how well the clustering represents sentiment. The proper method is to manually annotate a random subset of documents for the purpose of evaluation.

Also in general the second part with the classification model is not needed because the unsupervised clustering model can directly be applied to new instances.