Sentiment urgency emotion conversion over time for business intelligence

Purpose Social media has become a vital part of any institute’s marketing plan. Social networks benefit businesses by allowing them to interact with their clients, grow brand exposure through offers and promotions and find new leads. It also offers vital information concerning the general emotions and sentiments directly connected to the welfare and security of the online community involved with the brand. Big organizations can make use of their social media data to generate planned and operational decisions. This paper aims to look into the conversion of sentiments and emotions over time. Design/methodology/approach In this work, a model called sentiment urgency emotion detection (SUED) from previous work will be applied on tweets from two different periods of time, one before the start of the COVID-19 pandemic and the other after it started to monitor the conversion of sentiments and emotions over time. The model has been trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is high. This model will be tuned to improve results (Soussan and Trovati, 2020a; Soussan and Trovati, 2020b) and will be applied on a general business Twitter account of one of the largest chains of supermarkets in the UK to be able to see what sentiments and emotions can be detected and how urgent they are. Findings This will show the effect of COVID-19 pandemic on the conversions of the sentiments, emotions and urgencies of the tweets. Originality/value Sentiments will be compared between the two periods to evaluate how sentiments and emotions vary over time taking into consideration the COVID-19 as an affective factor. In addition, SUED will be tuned to enhance results and the knowledge that is mined when turning data into decisions is crucial because it will aid stakeholders handling the institute to evaluate the topics and issues that were mostly emphasized.

Modification of Prosody for Emotion Conversion using Gaussian Regression Model

Emotion conversion is one of the most inspiring forefronts of research in the arena of emotional speech synthesis. The main focus of the work is to convert a neutral speech sentence to the target emotional speech sentence using signal processing techniques. The parameters used for emotion conversion are pitch contour and intensity along with the duration of the sentence. Kannada Emotional Speech (KES) Database is created and used for analysis. The database consists of 4 (sadness, happy, anger, and fear) emotions with neutral. The pitch contour of different emotional sentences are analyzed and Gaussian Regression Model (GRM) is proposed for predicting the target pitch contour. The evaluation of the proposed method is done using Objective test & Subjective test. For objective test, mean pitch, the standard deviation of pitch, mean intensity and duration of the sentences are used. Evaluation using a subjective test is performed by calculating Emotion Recognition Rate (ERR) with the help of confusion matrix and also by taking the Mean Opinion Score (MOS) rating of the conversion system on the scale of 1-5. The result of Subjective test indicates that the effectiveness and discernment of emotion are improved when GRM is used for pitch contour modification with intensity and duration. The most recognized emotion was sadness with MOS of 3.52 and ERR of 83% and the least recognized emotion was anger with MOS of 1.74 and ERR of 66%. The results of the subjective and objective test show that the converted sadness, happy and fear speech is seeming very close to usual sadness, anger and fear emotion.