Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sentiment Analysis of Citations Using Word2vec   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.

8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph