# Improved Twitter Sentiment Prediction through Cluster-then-Predict Model

arXiv: https://arxiv.org/abs/1509.02437 · HTML: https://mlsanity.com/paper/1509.02437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Recommending Insurance products by using Users' Sentiments](https://mlsanity.com/paper/2108.06210/) (2021)

## Abstract (arXiv)

Over the past decade humans have experienced exponential growth in the use of online resources, in particular social media and microblogging websites such as Facebook, Twitter, YouTube and also mobile applications such as WhatsApp, Line, etc. Many companies have identified these resources as a rich mine of marketing knowledge. This knowledge provides valuable feedback which allows them to further develop the next generation of their product. In this paper, sentiment analysis of a product is performed by extracting tweets about that product and classifying the tweets showing it as positive and negative sentiment. The authors propose a hybrid approach which combines unsupervised learning in the form of K-means clustering to cluster the tweets and then performing supervised learning methods such as Decision Trees and Support Vector Machines for classification.