Aspect-based Sentiment Analysis using Dependency Parsing

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

A Data Processing Method Based on Sequence Labelling and Syntactic Analysis for Extracting New Sentiment Words from Product Reviews

New sentiment words in product reviews are valuable resources that are directly close to users. The data processing of new sentiment word extraction can provide information service better for users, and provide theoretical support for the related research of edge computing. Traditional methods for extracting new sentiment words generally ignored the context and syntactic information, which leads to the low accuracy and recall rate in the process of extracting new sentiment words. To tackle the mentioned issue, we proposed a data processing method based on sequence labeling and syntactic analysis for extracting new sentiment words from product reviews. Firstly, the probability that the new word is a sentiment word is calculated through the location rules derived from the sequence labeling result, and the candidate set of new sentiment words is obtained according to the probability. Then, the candidate set of new sentiment words is supplemented with the method of matching appositive words based on edit distance. Finally, the final set of new sentiment words is collected through fine-grained filtering, including the calculation of Point Mutual Information (PMI) and difference coefficient of positive and negative corpus (DC-PNC). The experimental results illustrate the effectiveness of new sentiment words extracted by the proposed method which can obviously improve the accuracy and recall rate of sentiment analysis.

GINS: A Global intensifier-based N-Gram sentiment dictionary

Words are one of the most essential elements of expressing sentiments in context although they are not the only ones. Also, syntactic relationships between words, morphology, punctuation, and linguistic phenomena are influential. Merely considering the concept of words as isolated phenomena causes a lot of mistakes in sentiment analysis systems. So far, a large amount of research has been conducted on generating sentiment dictionaries containing only sentiment words. A number of these dictionaries have addressed the role of combinations of sentiment words, negators, and intensifiers, while almost none of them considered the heterogeneous effect of the occurrence of multiple linguistic phenomena in sentiment compounds. Regarding the weaknesses of the existing sentiment dictionaries, in addressing the heterogeneous effect of the occurrence of multiple intensifiers, this research presents a sentiment dictionary based on the analysis of sentiment compounds including sentiment words, negators, and intensifiers by considering the multiple intensifiers relative to the sentiment word and assigning a location-based coefficient to the intensifier, which increases the covered sentiment phrase in the dictionary, and enhanced efficiency of proposed dictionary-based sentiment analysis methods up to 7% compared to the latest methods.

Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statements

The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis. Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the 10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at predicting future returns, the framework uses the alphalens library to perform factor return analysis, turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to this paper have been open-sourced on Github1.