# Sentiment Analysis of Citations Using Word2vec

arXiv: https://arxiv.org/abs/1704.00177 · HTML: https://mlsanity.com/paper/1704.00177/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2017.

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

- [HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2304.06910/) (2023)
- [On the Identification of the Energy related Issues from the App Reviews](https://mlsanity.com/paper/2304.11292/) (2023)
- [NLP Meets RNA: Unsupervised Embedding Learning for Ribozymes with Word2Vec](https://mlsanity.com/paper/2307.05537/) (2023)

## Abstract (arXiv)

Citation sentiment analysis is an important task in scientific paper analysis. Existing machine learning techniques for citation sentiment analysis are focusing on labor-intensive feature engineering, which requires large annotated corpus. As an automatic feature extraction tool, word2vec has been successfully applied to sentiment analysis of short texts. In this work, I conducted empirical research with the question: how well does word2vec work on the sentiment analysis of citations? The proposed method constructed sentence vectors (sent2vec) by averaging the word embeddings, which were learned from Anthology Collections (ACL-Embeddings). I also investigated polarity-specific word embeddings (PS-Embeddings) for classifying positive and negative citations. The sentence vectors formed a feature space, to which the examined citation sentence was mapped to. Those features were input into classifiers (support vector machines) for supervised classification. Using 10-cross-validation scheme, evaluation was conducted on a set of annotated citations. The results showed that word embeddings are effective on classifying positive and negative citations. However, hand-crafted features performed better for the overall classification.