Performing target-oriented opinion word extraction (TOWE) from online travel reviews is a valuable reference for both tourists and attraction administration department. This paper formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus. Learning target-oriented representation accurately, locating the opinion word and extracting the complete opinion are three major challenges. Hence, we leverage aspect-based query, pos-tag and relative position and devise appropriate structure to fuse them in an encoder-decoder framework. Specifically, in the encoder, the target-fused (aspect, review) pair and the pos-tag label are encoded by transformers to model the global dependency, in the decoder, a BiLSTM is adopted to enhance contextual representation by incorporating relative position information. A real-world Chinese travel dataset for TOWE task is constructed, and the experimental results demonstrate the efficacy of the proposed model. Extensive ablation experiments are also conducted to study the effect of different components of the model.

A Transformer-based Approach for Identifying Target-oriented Opinions from Travel Reviews

Research on Online Review Classification Based on Improved Sentiment Analysis

Performing target-oriented opinion word extraction (TOWE) from online travel reviews is a valuable reference for both tourists and attraction administration department. This paper formulates a novel research topic of identifying target-opinion pair from Chinese travel review corpus. Learning target-oriented representation accurately, locating the opinion word and extracting the complete opinion are three major challenges. Hence, we leverage aspect-based query, pos-tag and relative position and devise appropriate structure to fuse them in an encoder-decoder framework. Specifically, in the encoder, the target-fused (aspect, review) pair and the pos-tag label are encoded by transformers to model the global dependency, in the decoder, a BiLSTM is adopted to enhance contextual representation by incorporating relative position information. A real-world Chinese travel dataset for TOWE task is constructed, and the experimental results demonstrate the efficacy of the proposed model. Extensive ablation experiments are also conducted to study the effect of different components of the model. 

Considering the application of a sentiment analysis in decision-making and personalized advertising, we adopt it in tourism. Specifically, we perform a sentiment analysis on the posted Weibos about the passengers’ experience in civil aviation travel. Different travel events could influence passengers’ sentiment, e.g., flight delay may cause negative sentiment. Inspired by this observation, we propose a novel multimodal event-aware network to analyze sentiment from Weibos that contain multiple modalities, i.e., text and images. We first extract features from each modality and, then, model the cross-modal associations to obtain more discriminative representations, based on which we simultaneously perceive the event and sentiment in a multitask framework. Extensive experiments demonstrate that the proposed method outperforms the existing state-of-the-art approaches.

Lijuan Wang

Papers

Multimodal Event-Aware Network for Sentiment Analysis in Tourism