Statistical Inferences for Polarity Identification in Natural Language

Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

team UTB-NLP at finances 2023: financial targeted sentiment analysis using a phonestheme semantic approach

Sentiment analysis in the financial domain is a challenging task that plays a crucial role in understanding public opinion, monitoring market trends, and assessing the impact of news on economic agents. In this shared task, we address targeted sentiment analysis in the financial domain, focusing on identifying the main economic target in news headlines and determining the sentiment polarity towards such targets. We propose a methodology that combines transformer-based models and phonestheme embeddings to extract meaningful features from the text, which are then used in a support vector machine (SVM) classifier for sentiment classification. Our approach shows promising results, outperforming the baseline with an F1-score of 0.529229 in Task 1. This research contributes to financial sentiment analysis by addressing the complexity of financial language and considering multiple economic agents' perspectives.Universidad Tecnológica de Bolíva

Sentiment Polarity Identification of Social Media content using Artificial Neural Networks

Sentiment of people about consumer goods and government policies for decision making is normally collected through feedback forms, surveys etc. The social network sites and micro blogging sites are considered a very good source of information nowadays because people share and discuss their opinions about a certain topic freely. With the increased use of technology and social media, people proactively express their opinion through social media sites like Twitter, Facebook, Instagram etc. A social media sentiment analysis can help companies to understand how people feel about their products. On the other hand, extracting the sentiment from social media text is a challenging task due to the complexity of natural language processing of social media language. Often these messages reflect the emotion, opinion and sentiment of the public through a mix of text, image, emoticons etc. These statements are often called electronic Word of Mouth (eWOM) and are much prevalent in business and service industry to enable customers to share their point of view