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.

Big Data är i dagsläget ett populärt ämne som kan användas för en mängd olika syften. Bland annat kan det användas för att analysera data på webben i hopp om att identifiera brott mot mänskliga rättigheter. Genom att tillämpa tekniker inom områden som Artificiell Intelligens (AI), Information Retrieval (IR) samt data- visualisering, hoppas företaget Globalworks AB kunna identifiera röster vilka uttrycker sig om förtryck och kränkningar i social media. Artificiell intelligens och informationshämtning är dock breda områden och forskning som behandlar dem kan finnas långt tillbaka i tiden. Vi har därför valt att utföra en systematisk litteraturstudie i syfte att kartlägga existerande forskning inom dessa områden. Med en litterär sammanställning bistår vi med en ontologisk överblick i hur ett system som använder dessa tekniker är strukturerat, med vilka metoder och teknologier ett sådant system kan utvecklas, samt hur dessa kan kombineras. / Big Data is a popular topic these days which can be utilized for numerous purposes. It can, for instance, be used in order to analyse data made available online in hopes of identifying violations against human rights. By applying techniques within such areas as Artificial Intelligence (AI), Information Retrieval (IR), and Visual Analytics, the company Globalworks Ltd. aims to identify single voices in social media expressing grievances concerning such violations. Artificial Intelligence and Information Retrieval are broad topics however, and have been an active area of research for quite some time. We have therefore chosen to conduct a systematic literature review in hopes of mapping together existing research covering these areas. By presenting a literary compilation, we provide an ontological view of how an information system utilizing techniques within these areas could be structured, in addition to how such a system could deploy said techniques.