Construction method of fine-grained sentiment lexicon based on tourism field

Sentiment dictionaries are essential for sentiment analysis in tourism reviews since they provide valuable prior knowledge for identifying lexical emotions. The selection criteria for seed word sets in the conventional method of building the domain sentiment dictionary typically only utilize semantic vectors or term frequency statistics, which leaves the seed word set with insufficient emotional representation and, consequently, impairs the vocabulary emotion recognition accuracy. Therefore, we propose a method of sentiment seed word set selection based on a multiple feature fusion strategy and emoji integration. This method integrates corpus statistical features, emotional intensity features, and lexical semantic features as the screening criteria for various emotional seed word sets, ensuring a high match between seed vocabulary and corpus characteristics, improving the representativeness and coverage of the seed word set effectively. At the same time, emoticons are introduced to enhance the emotional expressive capabilities of the seed set and improve the accuracy of emotional classification of vocabulary by adding emotional aspects that emotional vocabulary might overlook. Finally, it constructed a fine-grained sentiment dictionary for the tourism field. According to experiments, sentiment analysis employing the tourism field’s sentiment dictionary enhances the accuracy rate by 0.0949, recall rate by 0.0896, and F₁ value by 0.0923 on average when compared to other Chinese general dictionaries in the tourism corpus.