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We revisit the discussion of market sentiment in European sovereign bonds using a correlation analysis toolkit based on influence networks and hierarchical clustering. We focus on three case studies of political interest. In the case of the 2016 Brexit referendum, the market showed negative correlations between core and periphery only in the week before the referendum. Before the French presidential elections in 2017, the French bond spread widened together with the estimated Le Pen election probability, but the position of French bonds in the correlation blocks did not weaken. In summer 2018, during the budget negotiations within the new Italian coalition, the Italian bonds reacted very sensitively to changing political messages but did not show contagion risk to Spain or Portugal for several months. The situation changed during the week from October 22 to 26, as a spillover pattern of negative sentiment also to the other peripheral countries emerged.

AbstractThe inherent qualitative nature of textual data poses significant challenges for direct integration into statistical models. This paper presents a two-stage process for analyzing longitudinal textual data, offering a solution to this inherent challenge. The proposed model comprises (1) initial data preprocessing and sentiment extraction, followed by (2) applying a growth curve model to analyze the extracted sentiments directly. The paper also explores four distinct approaches for extracting sentiment scores in the dialogue, providing versatility to the proposed framework. The practical application of the proposed model is demonstrated through the analysis of an empirical longitudinal textual dataset. This framework offers a valuable contribution to the field by addressing the challenges associated with modeling qualitative textual data, providing a robust methodology for extracting and analyzing sentiments longitudinally.

Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era's possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country's passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh's social, political, and law-and-order situation, particularly during election season and the country's cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner.

International Journal of Advanced Computer Science and Applications(IJACSA), 8(6), 2017 ; The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc. ; http://thesai.org/Downloads/Volume8No6/Paper_57-Sentiment_Analysis_using_Deep_Learning.pdf

"This book explores what drives profitability and more successful customer relationship management. It looks the behavior of any classifier used to classify sentiments in product reviews. It also develops common expert system to classify and visualize the predicted result"--

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

AbstractAudio sentiment analysis is a growing area of research, however little attention has been paid to the fairness of machine learning models in this field. Whilst the current literature covers research on machine learning models' reliability and fairness in various demographic groups, fairness in audio sentiment analysis with respect to gender is still an uninvestigated field. To fill this knowledge gap, we conducted experiments aimed at assessing the fairness of machine learning algorithms concerning gender within the context of audio sentiment analysis. In this research, we used 442 audio files of happiness and sadness—representing equal samples of male and female subjects—and generated spectrograms for each file. Then we performed feature extraction using bag-of-visual-words method followed by building classifiers using Random Forest, Support Vector Machines, and K-nearest Neighbors algorithms. We investigated whether the machine learning models for audio sentiment analysis are fair across female and male genders. We found the need for gender-specific models for audio sentiment analysis instead of a gender-agnostic-model. Our results provided three pieces of evidence to back up our claim that gender-specific models demonstrate bias in terms of overall accuracy equality when tested using audio samples representing the other gender, as well as combination of both genders. Furthermore, gender-agnostic-model performs poorly in comparison to gender-specific models in classifying sentiments of both male and female audio samples. These findings emphasize the importance of employing an appropriate gender-specific model for an audio sentiment analysis task to ensure fairness and accuracy. The best performance is achieved when using a female-model (78% accuracy) and a male-model (74% accuracy), significantly outperforming the 66% accuracy of the gender-agnostic model.

Nowadays individuals as well as institutions are paying increasing attention to sentiment analysis. Companies are interested in what bloggers are saying about their products. Politicians are interested in how different news media are portraying them. Governments are interested in how foreign news media are representing their actions. An automatic method is thus needed that is capable of processing and analyzing the information. In my research proposing for the first time, an Adverb-adjective- noun combinations (AAN) based sentiment analysis technique deploying linguistic analysis of adverbs of degree, adjective and abstract noun. Here define a set of general axioms (based on a classification of adverbs of degree into five categories, classification of abstract noun in two categories) for opinion analysis. This has been a significant advancement from the previous research on this domain. There is currently no automated domain-independent sentiment classification tool, with high accuracy that does not need a manually-annotated corpus. Such a tool is needed for opinion search, recommendation, summarization and mining of the increasingly web opinionated content.Keywords: Sentiment analysis, Adverb-adjective-noun combination, adverbs of degree, abstract noun, domain-independent sentiment classification tool

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.

BASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169 ; Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naive Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.