Mood analysis can be used for any type of survey, quantitatively and qualitatively, and for customer support interactions to understand your clients` emotions and opinions. Tracking the customer`s mood over time increases the depth to understand why NPS scores or feelings have changed depending on certain aspects of your business. If you want to start with these immediately ready-to-use tools, read this manual on the best SaaS tools for mood analysis, which are also provided with APIs for seamless integration with your existing tools. Nevertheless, mood analysis is worth it, even if your mood analysis forecasts are wrong from time to time. If you use MonkeyLearn`s mood analysis model, you can expect about 70-80% of the time you submit your texts to the classification of correct predictions. As clients express their thoughts and feelings more openly than ever before, mood analysis becomes an essential tool for monitoring and understanding this mood. Automatic analysis of customer feedback, for example. B opinions in survey responses and conversations on social networks, allows brands to know what makes customers happy or frustrated in order to tailor products and services to the needs of their customers. If a comment contained a number of separate statements, we used the principle of composite evaluation. This means that each statement was evaluated by itself, while the final mood label was obtained by combining these labels. This field represents the co-assessment attributed to the mood analysis of the text. Its value is quite a figure in the 0-100 range.

Funding: This work was funded in part by the Ministry of Education, Science and Technological Development of the Republic of Serbia under Project III 44009 ( The mood assessment process was supported by the Regional Language Data Initiative (ReLDI) project through the Swiss National Fund`s Scholarship 160501 ( This research was also supported by the Scientific Fund of the Republic of Serbia, Scholarship No. 6526093, AI РAVANTES ( Funders played no role in the organization of the studies, the collection and analysis of the data, the decision to publish or prepare the manuscript. The authors thank Aleksandar Milinkovic for his support of the lemmatisation srWaC and Ognjen Kresai, who acted as one of the main nocturators. We would also like to thank Ana Bjelogrli̩, Jelena Boenjak, Filip Maljkovic and Marko Jankovic for their participation in the atmosphere. The First Author also served as one of the main indicators. Finally, we thank Ana Bjelogrli̩ for correcting the text. The applications of mood analysis are endless and can be applied to any sector, from finance and retail to hospitality and catering and technology.

Below, we`ve listed some of the most popular methods used for mood analysis in companies: If you`re interested in a rules-based approach, you`ll find below a varied list of helpful mood analysis lexi principals. These lexipics provide a series of dictionaries with captions that indicate their moods in different areas. The following lexipics are really useful for identifying the mood of the texts: Tables 1 and 2 contain the intragroup chord percentages per pair and alpha scores for all these label interpretations and the three groups of annotations in the film or in the book verification file. They also contain alpha intergroup scores that represent match levels between different pairs of groups. Unlike the percentages of simple chords, Krippendorff`s alpha scores take into account random agreement, so the rest of our discussion will focus on this.