# Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations

## TL;DR

This paper presents a hybrid approach combining rule-based, semantic, and neural methods to improve negation detection and sentiment analysis in customer service conversations on Twitter.

## Contribution

It introduces a novel negation scope detection algorithm tailored for conversational data and an antonym dictionary-based sentiment model that outperforms existing methods.

## Key findings

- Negation scope detection achieves comparable results to state-of-the-art models.
- Antonym-based sentiment analysis outperforms lexicon and neural network approaches.
- The approach enhances sentiment understanding in customer service interactions.

## Abstract

Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of negation in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a CNN-LSTM combination model for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and neural network methods.