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

Stock market sentiment lexicon acquisition using microblogging data and statistical measures

Lexicon acquisition is a key issue for sentiment analysis. This paper presents a novel and fast approach for creating stock market lexicons. The approach is based on statistical measures applied over a vast set of labeled messages from StockTwits, which is a specialized stock market microblog. We compare three adaptations of statistical measures, such as pointwise mutual information (PMI), two new complementary statistics and the use of sentiment scores for affirmative and negated con- texts. Using StockTwits, we show that the new lexicons are competitive for measuring investor sentiment when compared with six popular lexicons. We also applied a lexicon to easily produce Twitter investor sentiment indicators and analyzed their correlation with survey sentiment indexes. The new microblogging indicators have a moderate correlation with popular Investors Intelligence (II) and American Association of Individual Investors (AAII) indicators. Thus, the new microblogging approach can be used alternatively to traditional survey indicators with advantages (e.g., cheaper creation, higher frequencies).This work was supported by FCT - Funda ção para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/201

Questioning the news about economic growth : sparse forecasting using thousands of news-based sentiment values

The modern calculation of textual sentiment involves a myriad of choices as to the actual calibration. We introduce a general sentiment engineering framework that optimizes the design for forecasting purposes. It includes the use of the elastic net for sparse data-driven selection and the weighting of thousands of sentiment values. These values are obtained by pooling the textual sentiment values across publication venues, article topics, sentiment construction methods, and time. We apply the framework to the investigation of the value added by textual analysis-based sentiment indices for forecasting economic growth in the US. We find that the additional use of optimized news-based sentiment values yields significant accuracy gains for forecasting the nine-month and annual growth rates of the US industrial production, compared to the use of high-dimensional forecasting techniques based on only economic and financial indicators. (C) 2018 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters

Sentiment analytics: Lexicons construction and analysis

With the increasing amount of text data, sentiment analysis (SA) is becoming more and more important. An automated approach is needed to parse the online reviews and comments, and analyze their sentiments. Since lexicon is the most important component in SA, enhancing the quality of lexicons will improve the efficiency and accuracy of sentiment analysis. In this research, the effect of coupling a general lexicon with a specialized lexicon (for a specific domain) and its impact on sentiment analysis was presented. Two special domains and one general domain were studied. The two special domains are the petroleum domain and the biology domain. The general domain is the social network domain. The specialized lexicon for the petroleum domain was created as part of this research. The results, as expected, show that coupling a general lexicon with a specialized lexicon improves the sentiment analysis. However, coupling a general lexicon with another general lexicon does not improve the sentiment analysis --Abstract, page iii

Semi-automatic approaches for exploiting shifter patterns in domain-specific sentiment analysis

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction

Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

In this paper, we automatically create senti- ment dictionaries for predicting financial out- comes. We compare three approaches: (i) manual adaptation of the domain-general dic- tionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first man- ual, then automatic adaptation. In our experi- ments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the finan- cial outcomes excess return and volatility. In particular, automatic adaptation performs bet- ter than manual adaptation. In our analysis, we find that annotation based on an expert’s a priori belief about a word’s meaning can be incorrect – annotation should be performed based on the word’s contexts in the target do- main instead

Identifying Polarity in Financial Texts for Sentiment Analysis: A Corpus-based Approach

AbstractIn this paper we describe our methodology to integrate domain-specific sentiment analysis in a lexicon-based system initially designed for general language texts. Our approach to dealing with specialized domains is based on the idea of “plug-in” lexical resources which can be applied on demand. A simple 3-step model based on the weirdness ratio measure is proposed to extract candidate terms from specialized corpora, which are then matched against our existing general-language polarity database to obtain sentiment-bearing words whose polarity is domain-specific

Algorithmic trading with cryptocurrencies - does twitter sentiment impact short-term price fluctuations in bitcoin

Since its inception in 2009, Bitcoin has gained popularity and importance in financial markets. The Bitcoin price is highly volatile entailing high risk and chances of high returns for traders. This work is part of a work project, which performs a holistic approach to build an intra day Bitcoin trading algorithm based on predictive analysis of Machine Learning models. This part performs a Sentiment Analysis on Twitter data, showing a Granger causal relationship between the extracted Sentiment and the Bitcoin price

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis