# Task-specific Word Identification from Short Texts Using a Convolutional Neural Network

## TL;DR

This paper introduces a CNN-based method for identifying task-specific words in short texts using class labels, outperforming existing approaches that rely on seed words or lexical resources.

## Contribution

The paper presents a novel CNN-based approach that leverages labeled short texts to identify task-specific words without needing seed words or lexical dictionaries.

## Key findings

- Significantly outperforms existing methods in sentiment word identification.
- Effectively captures discrimination-related words in tweets.
- Successfully identifies fake-review words in case studies.

## Abstract

Task-specific word identification aims to choose the task-related words that best describe a short text. Existing approaches require well-defined seed words or lexical dictionaries (e.g., WordNet), which are often unavailable for many applications such as social discrimination detection and fake review detection. However, we often have a set of labeled short texts where each short text has a task-related class label, e.g., discriminatory or non-discriminatory, specified by users or learned by classification algorithms. In this paper, we focus on identifying task-specific words and phrases from short texts by exploiting their class labels rather than using seed words or lexical dictionaries. We consider the task-specific word and phrase identification as feature learning. We train a convolutional neural network over a set of labeled texts and use score vectors to localize the task-specific words and phrases. Experimental results on sentiment word identification show that our approach significantly outperforms existing methods. We further conduct two case studies to show the effectiveness of our approach. One case study on a crawled tweets dataset demonstrates that our approach can successfully capture the discrimination-related words/phrases. The other case study on fake review detection shows that our approach can identify the fake-review words/phrases.