Sentiment Difficulty in Aspect-Based Sentiment Analysis

Abstract

Subjectivity is a key aspect of natural language understanding, especially in the context of user-generated text and conversational systems based on large language models. Natural language sentences often contain subjective elements, such as opinions and emotions, that make them more nuanced and complex. The level of detail at which the study of the text is performed determines the possible applications of sentiment analysis. The analysis can be done at the document or paragraph level, or, even more granularly, at the aspect level. Many researchers have studied this topic extensively. The field of aspect-based sentiment analysis has numerous data sets and models. In this work, we initiate the discussion around the definition of sentence difficulty in this context of aspect-based sentiment analysis. To assess and quantify the difficulty of the aspect-based sentiment analysis, we conduct an experiment using three data sets: “Laptops”, “Restaurants”, and “MTSC” (Multi-Target-dependent Sentiment Classification), along with 21 learning models from scikit-learn. We also use two textual representations, TF-IDF (Terms frequency-inverse document frequency) and BERT (Bidirectional Encoder Representations from Transformers), to analyze the difficulty faced by these models in performing aspect-based sentiment analysis. Additionally, we compare the models with a fine-tuned version of BERT on the three data sets. We identify the most challenging sentences using a combination of classifiers in order to better understand them. We propose two strategies for defining sentence difficulty. The first strategy is binary and considers sentences as difficult when the classifiers are unable to correctly assign the sentiment polarity. The second strategy uses a six-level difficulty scale based on how many of the top five best-performing classifiers can correctly identify sentiment polarity. These sentences with assigned difficulty classes are then used to create predictive models for early difficulty detection. The purpose of estimating the difficulty of aspect-based sentiment analysis is to enhance performance while minimizing resource usage.

Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet

“…Fine-tuning [20] the Mongolian phoneme information combined with contextual information through the Bang pre-training model not only reduces the training complexity of the Mongolian speech synthesis model, but also reduces the probability of wrong words and missing words in the generated speech, making the generated speech more accurate and natural, greatly improving the quality of the generated speech. The data experiment and result analysis of the Mongolian phoneme pre-training model are given in Section 4.4.…”

Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism

“…With the rapid development of the Internet, the problem of information overload [1] has become more and more serious, and recommendation systems [2] play a pivotal role in a variety of online services, which can help users quickly and efficiently select the information they need in a huge amount of information, such as product recommendations on e-commerce platforms, movie recommendations on video websites, and natural language processing (NLP)-related tasks [3][4][5][6].…”