# Emotion Recognition Method for Call/Contact Centre Systems
## 3 Research Methodology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Description** & **Application** \\ \hline DBT1 & 345 chat text conversations (7515 statements of clients and agents were extracted). & Data used in the process of training and testing of classifiers in part one. \\ \hline DBT2 & 100 chat text conversations (3718 statements of clients and agents were extracted). & Database used in the verification process of the developed method. \\ \hline DBV1 & 302 actual voice calls ranging from & Data used in the process of training and testing of classifiers in part two. \\  & 3 to 20 min in duration. The total recording time of this database is 2 h 59 min and 11 s. & Database used in the verification process of the developed method. \\ \hline DBV2 & 100 actual voice calls ranging from 3 to 20 min in duration. The total recording time of this database is 7 h 25 min and 15 s. & Additional functionality to improve recognition for the problem in part two. \\ \hline DBVT1 & Database of transcriptions of recordings from DBV1 set. & Database used in the verification process of the developed method. \\ \hline DBVT2 & Database of transcriptions of recordings from DBV2 set. & Database used in the verification process of the developed method. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Used databases. Figure 1: Research methodology. to detect emotions in voice channels. The judges independently evaluated the extracted data and, as a result of the final discussion, determined a single outcome score for each statement. Finally, the pool of audio and text files (used in the training and testing process) was joined by those for which there was 100% agreement in the judges' evaluation. Thanks to the evaluations obtained and taking into account the opinions of people with experience in the operation, functioning and implementation of CC systems, it was possible to propose the Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents in voice and text channels of contact centre systems [54]. Accordingly, classes of basic emotions (neutral, happiness, sadness, fear, anger) were defined, corresponding to families of related emotions identified in the databases: DBT1 and DBV1/DBV11. The emotion classes thus defined are recognized by the solution proposed in this paper. The problem is therefore multiclass, with five classes. In the next phase of the work, sets for the training and testing processes were created from the conversation fragments selected in the data preparation process for the DBT1 and DBV1 databases. In terms of chat conversations (DBT1 database), the original set consisted of 7515 utterances, of which 3766 items were removed as a result of the evaluation procedure described above. The deleted items were 463 utterances containing text from the anonymization process and 3303 utterances that were not explicitly marked by the judges. Thus, the remaining 3749 utterances were used as the data for the training and testing processes. From these utterances, a total of the following emotion classes were selected: neutral = 2269, happiness = 761, anger = 312, sadness = 305, and fear = 102. In terms of voice conversations (DBV1 database), the judges convergently marked a total of 400 different passages in which specific emotion types were present. As the durations of the utterances marked by the judges were of variable length, the long passages were divided into several parts, which ultimately allowed the construction of a set consisting of 2935 elements. The class sizes were as follows: neutral = 1491, anger = 937, happiness = 196, fear = 166, sadness = 145. The created datasets are characterized by relatively large discrepancies in sample sizes across classes. The problem of quantitatively unbalanced data in the training set can significantly affect the training processes, which in turn is later reflected in the classification results.