# Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews

arXiv: https://arxiv.org/abs/1412.5335 · HTML: https://mlsanity.com/paper/1412.5335/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2014.

## Built on by (most cited first, 5 of 5)

- [The Document Vectors Using Cosine Similarity Revisited](https://mlsanity.com/paper/2205.13357/) (2022)
- [Deep Learning-based Sentiment Classification: A Comparative Survey](https://mlsanity.com/paper/2312.17253/) (2023)
- [Sentiment Analysis of Movie Reviews Using BERT](https://mlsanity.com/paper/2502.18841/) (2025)
- [Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis](https://mlsanity.com/paper/2502.20682/) (2025)
- [Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing](https://mlsanity.com/paper/2105.11798/) (2021)

## Abstract (arXiv)

Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several ma- chine learning approaches to this problem, and combine them to achieve the best possible results. We show how to use for this task the standard generative lan- guage models, which are slightly complementary to the state of the art techniques. We achieve strong results on a well-known dataset of IMDB movie reviews. Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state of the art, as other researchers can combine their techniques with ours with little effort.