# Emotion Recognition on Call Center Voice Data
## 3 Materials and Methods
### Dataset Properties  
The dataset used in the project was created from a training dataset comprising 520,000 sentences, while the test dataset contained 48,000 sentences. A distinct dataset utilized in the validation phase comprised 100,000 sentences and is affiliated with Turkcell. The accuracy rate was determined using the primary test dataset from which the results were derived. This dataset was carefully prepared for emotional analysis and designed to have an unbalanced label distribution. We have to declare that this dataset belongs to Turkcell (Istanbul, Turkley). The audio files are encoded in 8-bit channel WAV format. This format offers an efficient framework for managing and examining audio data.  
The audio data were transcribed into text utilizing Turkcell's proprietary speech-to-text algorithm. Turkcell experts also verified the accuracy of the transcription process during the labeling phase.  
The labeling procedure was executed by experts from the Turkcell call center. This process verified the accuracy of audio data transcription and assessed whether the speech conveyed the emotional context. Experts conducted tagging by verifying that the speech was accurately transcribed and that the underlying emotional tone was appropriately conveyed.  
Audio files are stored in the cloud. MFCC features are derived from audio data utilizing the Java programming language. The TarsoSDI library utilized in Java is favored for computing the MFCC feature vectors of audio data. The extracted MFCC features are subsequently converted to CSV format and stored. Each CSV file comprises MFCC features and corresponding labels, along with specified links to the audio files. These data serve as the primary source for the system's training and testing phases.  
This study utilized real-world data sourced from a dataset containing 100,000 call center interactions supplied by Turkcell Global Bilgi. This dataset contains authentic customer interactions from Turkcell and was utilized during the model's testing and validation phases. The tests conducted on these data yielded a high accuracy rate through the integration of text and voice-based classification models.  
The most common words that occur in the dataset are:  
* \"bir\" (one) (213,794 times)
* \"ve\" (and) (180,040 times)
* \"cok\" (very) (147,731 times)
* \"bu\" (this) (103,081 times)
* \"icin\" (for) (64,202 times)
* \"urun\" (product) (57,019 times)
* \"daha\" (more) (53,731 times)
* \"ama\" (but) (52,372 times)
* \"da\" (also) (50,740 times)  
The number of unique words that occur in the dataset is 666,748 unique words, indicating that the model was trained with a large vocabulary. Table 1 shows the distribution of sentence lengths in the dataset, and Table 2 shows the label distribution in the dataset.  
The label distribution in the dataset is unbalanced, with fewer negative emotions. This distribution reflects the situation where correct classification of negative emotions is critical, especially in applications where negative feedback is essential, such as customer service. The smaller number of negative emotions is a deliberate choice to make the model more sensitive to this class and increase its ability to recognize such labels correctly. This approach aims to increase the overall accuracy and improve the model's performance on more challenging tasks, such as correctly detecting negative emotions. These dataset features and label distribution are vital to the model's training process and subsequent emotion classification success.