Background Daily, companies generate enormous amounts of customer support tickets which are grouped and placed in specialised queues, based on some characteristics, from where they are resolved by the customer support personnel (CSP) on a first-in-first-out basis. Given that these tickets require different levels of urgency, a logical next step to improving the effectiveness of the CSPs is to prioritise the tickets based on business policies. Among the several heuristics that can be used in prioritising tickets is sentiment polarity. Objectives This study investigates how machine learning methods and natural language techniques can be leveraged to automatically predict the sentiment polarity of customer support tickets using. Methods Using a formal experiment, the study examines how well Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) based sentiment polarity prediction models built for the product and movie reviews, can be used to make sentiment predictions on email support tickets. Due to the limited size of annotated email support tickets, Valence Aware Dictionary and sEntiment Reasoner (VADER) and cluster ensemble - using k-means, affinity propagation and spectral clustering, is investigated for making sentiment polarity prediction. Results Compared to NB and LR, SVM performs better, scoring an average f1-score of .71 whereas NB scores least with a .62 f1-score. SVM, combined with the presence vector, outperformed the frequency and TF-IDF vectors with an f1-score of .73 while NB records an f1-score of .63. Given an average f1-score of .23, the models transferred from the movie and product reviews performed inadequately even when compared with a dummy classifier with an f1-score average of .55. Finally, the cluster ensemble method outperformed VADER with an f1-score of .61 and .53 respectively. Conclusions Given the results, SVM, combined with a presence vector of bigrams and trigrams is a candidate solution for extracting sentiments from email support tickets. Additionally, transferring sentiment models from the movie and product reviews domain to the email support tickets is not possible. Finally, given that there exists a limited dataset for conducting sentiment analysis studies in the Swedish and the customer support context, a cluster ensemble is recommended as a sample selection method for generating annotated data.

Historians, mostly engaging with written evidence, have argued that the Christianisation of the Roman Empire resulted in changes in both attitudes and behaviour towards children, resulting in a decrease in their maltreatment by society. I begin with a working hypothesis that this attitude-change was real and resulted in a reduction in the maltreatment of children; and that this reduction in maltreatment is evident in the literature. The approach to investigating this hypothesis belongs to the emerging field of digital humanities: by using programming techniques developed in the field of sentiment analysis, I create two sentiment-analysis like tools, one a lexicon-based approach, the other an application of a naive bayes machine learning approach. The latter is favoured as more accurate. The tool is used to automatically tag sentences, extracted from a corpus of texts written between 100 B.C and 600 A.D, that mention children, as to whether the sentences feature the maltreatment of children or not. The results are then quantitively analysed with reference to the year in which the text was written, with no statistically significant result found. However, the high accuracy of the tool in tagging sentences, at above 88%, suggests that similar tools may be able to play an important role, alongside traditional research techniques, in historical and social-science research in the future.

Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques to analyze data. One such kind of data which is needed to be analyzed are text reviews given for movies. Rather than classifying the reviews as positive or negative, we will classify the sentiment of the reviews on the scale of one to ten. In doing so, we will compare two recurrent neural network algorithms Long short term memory(LSTM) and Gated recurrent unit(GRU). The main objective of this study is to compare the accuracies of LSTM and GRU models. For training models, we collected data from two different sources. For filtering data, we used porter stemming and stop words. We coupled LSTM and GRU with the convolutional neural networks to increase the performance. After conducting experiments, we have observed that LSTM performed better in predicting border values. Whereas, GRU predicted every class equally. Overall GRU was able to predict multiclass text data of movie reviews slightly better than LSTM. GRU was computationally expansive when compared to LSTM.

The intersection of the environment, the economy and society create a wicked problem in salmon aquaculture in Canada. To provide a unique insight into the challenges of the salmon aquaculture industry amongst key stakeholders, this thesis investigates the sentiment of several important stakeholder groups in the salmon aquaculture industry: academics, industry, ENGOs, Government, Indigenous peoples, and the media. By scraping data from Twitter from the years 2006 to 2021, it examines aquaculture sentiment from a global English-speaking view, as well as a subset of Canadian data. This thesis addresses the following questions: How does public sentiment towards salmon aquaculture differ over time? How does public sentiment towards salmon aquaculture differ among stakeholder groups? Data is analyzed through a stakeholder management theory framework using sentiment analysis. Data is collected from Twitter because users prefer it to other social media sites to share their unprompted thoughts, ideas, and opinions. The data is scrapable using the open-source Twitter scraper Twint. The data is processed using Google Colab notebooks: raw data is preprocessed into 273,319 tweets (rows) of clean data, which are analyzed using VADER’s natural language processing tool, yielding a sentiment score between -1 and +1 for each tweet. This thesis explores the dependent variable of sentiment and the independent variable of time. Findings are examined through the lens of overall sentiment, sentiment from year to year (2006-2021), sentiment per stakeholder category, and sentiment per stakeholder category per year. Sentiment from 2007 to 2021 is expected to be increasingly negative because of significant negative events in the salmon aquaculture industry from 2006 to 2021. There have been many policy changes, lawsuits, fish escapes and concerns from ENGOs, Indigenous groups, and researchers about salmon aquaculture during this time. However, the data contradicts this hypothesis by trending positively over time. The overall dataset is consistent and clusters around a mean of 0.3 (slightly positive), a median of 0.4 and a standard deviation of 0.4. The skewness of the general data is -0.994, meaning that the distribution has a moderate negative skew (most tweets have positive sentiment). The dataset has an R-squared value of 0.64, meaning that the data represents a moderate model, and an R-squared value of 0.79 (when removing outliers) shows an absolute strong model. All eight stakeholder group categories display a moderately negative skewness value and a positive mean sentiment. The Academic / Researcher Group and the Industry / Worker stakeholder groups show strong models, and the other stakeholder categories with lower R-squared values show weaker models. This thesis provides new insight into the growing and expanding salmon aquaculture industry. Further, understanding stakeholder sentiment can allow a government, individual, or group to be more proactive in its decision-making rather than reactive. The data allows for open dialogue with all stakeholders and promotes future research, analysis, and collaboration within the salmon aquaculture industry.

A major security risk with the use of a Java logging library called Log4j was discovered in November 2021. The vulnerability meant that all Java applications using Log4j could be exploited by hackers through remote code execution. The Log4j vulnerability came to the general public's knowledge and became a hot topic on various social media platforms the 9th of December 2021. This is what will be referred to as the Log4j incident in this paper. The aim of the study is to investigate what attitudes users on Twitter have towards Log4j and how these attitudes have evolved over time in relation to the incident in question. Twitter data regarding Log4j was collected using Twitter API and sentiment analysis was performed on the data set using VADER. The gathered tweets were classified as either positive, negative or neutral. The data was collected, sorted and analyzed based on the CRISP-DM methodology. Tweets from two different time periods were studied. The two periods were 1) five months prior to the incident and 2) five months after the incident. The results showed that tweets posted before the incident were mostly positive, while tweets posted after the incident were mostly negative. An interesting discovery was found when comparing the sentiments exhibited within the five-month period directly following the incident. During the first month the results exhibited a predominance of negative sentiment regarding Log4j, while April 2022 on the contrary, was predominantly positive. In conclusion this study has presented the results of the attitudes a large group of Twitter users have expressed towards Log4j and how these attitudes have evolved over time. A gap in related research of how the discussions on social media circulate when a security threat with great impact appears has been identified and this study aims to provide new insights within this area.

In the sustainable tourist sector today, there is a wide margin of loss in small and medium-sized enterprise (SMEs) because of a poor control in logistical expenses. In other words, acquired goods are note being sold, a scenario which is very common in tourism SMEs. These SMEs buy a number of travel packages to big companies and because of the lack of demand of said packages, they expire and they become an expense, not the investment it was meant to be. To solve this problem, we propose a Predictive model based on sentiment analysis of a social networks that will help the sales decision making. Once the data of the social network is analyzed, we also propose a prediction model of tourist destinations, using this information as data source it will be able to predict the tourist interest. In addition, a case study was applied to a real Peruvian tourist enterprise showing their data before and after using the proposed model in order to validate the feasibility of proposed model.

This thesis aims to understand the emotions expressed by adults watching animated films with death topics through sentiment analysis. The research is a quantitative sentiment analysis from the perspective of distant reading. The previous studies on death scenes in animated films have only focused on child audiences. However, the age group of the audience of animated films is extensive; thus, it is necessary to analyse the sentiments of adult audiences. This thesis attempts to collect two movies produced by Pixar studio: Coco (2017) and Soul (2020), as well as their audience reviews on IMDb, a total of 600, for cross-comparison. Additionally, it analyses the content containing death in the reviews to understand better adult audiences’ emotional expressions on the subject of death. The analysing results show that the positive sentiment scores of the comments containing death are slightly lower than the scores of all the reviews, and the scores of the negative sentiments do not differ much. However, positive emotions still dominate these comments that contain death. The emotional performance between the script and the reviews is roughly similar. Still, the emotional intensity of the comments is higher than that of the script, indicating that the audience is willing to show their emotions on the public online film platform. Future research is recommended to conduct analysis together with other NLP analysis methods or close reading to explore more details of the content.

Background: The implicit nature of social media information brings many advantages to realistic sentiment analysis applications. Sentiment Analysis is the process of extracting opinions and emotions from data. As a research topic, sentiment analysis of Twitter data has received much attention in recent years. In this study, we have built a model to perform sentiment analysis to classify the sentiments expressed in the Twitter dataset based on the public tweets to raise awareness of the public's concerns by training the models. Objectives: The main goal of this thesis is to develop a model to perform a sentiment analysis on the Twitter data regarding the COVID-19 vaccine and find out the sentiment’s polarity from the data to show the distribution of the sentiments as following: positive, negative, and neutral. A literature study and an experiment are set to identify a suitable approach to develop such a model. Time-series analysis performed to obtain daily sentiments over the timeline series and daily trend analysis with events associated with the particular dates. Method: A Systematic Literature Review is performed to identify the most suitable approach to accomplish the sentiment analysis on the COVID-19 vaccine. Then, through the literature study results, an experimental model is developed to distribute the sentiments on the analyzed data and identify the daily sentiments over the timeline series. Result: A VADER is identified from the Literature study, which is the best suitable approach to perform the sentiment analysis. The KDE distribution is determined for each sentiment as obtained by the VADER Sentiment Analyzer. Daily sentiments over the timeline series are generated to identify the trend analysis on Twitter data of the COVID-19 vaccine. Conclusion: This research aims to identify the best-suited approach for sentiment analysis on Twitter data concerning the selected dataset through the study of results. The VADER model prompts optimal results among the sentiments polarity score for the sentiment analysis of Twitter data regarding the selected dataset. The time-series analysis shows how daily sentiments are fluctuant and the daily counts. Seasonal decomposition outcomes speak about how the world is reacting towards the current COVID-19 situation and daily trend analysis elaborates on the everyday sentiments of people.

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In this study we gather customer reviews from Prisjakt, a Swedish price comparison site, with the goal to study the relationship between review and rating, known as sentiment analysis. The purpose of the study is to evaluate three different supervised machine learning models on a fine-grained dependent variable representing the review rating. For classification, a binary and multinomial model is used with the one-versus-one strategy implemented in the Support Vector Machine, with a linear kernel, evaluated with F1, accuracy, precision and recall scores. We use Support Vector Regression by approximating the fine-grained variable as continuous, evaluated using MSE. Furthermore, three models are evaluated on a balanced and unbalanced dataset in order to investigate the effects of class imbalance. The results show that the SVR performs better on unbalanced fine-grained data, with the best fine-grained model reaching a MSE 4.12, compared to the balanced SVR (6.84). The binary SVM model reaches an accuracy of 86.37% and weighted F1 macro of 86.36% on the unbalanced data, while the balanced binary SVM model reaches approximately 80% for both measures. The multinomial model shows the worst performance due to the inability to handle class imbalance, despite the implementation of class weights. Furthermore, results from feature engineering shows that SVR benefits marginally from certain regex conversions, and tf-idf weighting shows better performance on the balanced sets compared to the unbalanced sets.