# A Comprehensive Overview of Recommender System and Sentiment Analysis

arXiv: https://arxiv.org/abs/2109.08794 · HTML: https://mlsanity.com/paper/2109.08794/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 152 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews](https://mlsanity.com/paper/1802.07938/) (2018)
- [W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/1705.07687/) (2017)
- [Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer](https://mlsanity.com/paper/2006.09235/) (2020)

## Built on by (most cited first, 3 of 3)

- [TEARS: Textual Representations for Scrutable Recommendations](https://mlsanity.com/paper/2410.19302/) (2024)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)
- [Debiased Estimators in High-Dimensional Regression: A Review and Replication of Javanmard and Montanari (2014)](https://mlsanity.com/paper/2604.00848/) (2026)

## Abstract (arXiv)

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.