Cyberbullying is a growing problem in our society that can bring fatal consequences and can be presented in digital text for example at online social networks. Nowadays there is a wide variety of works focused on the detection of digital texts in the English language, however in the Spanish language there are few studies that address this issue. This paper aims to detect this cybernetic harassment in social networks, in Spanish language. Sentiment analysis techniques are used, such as bag of words, elimination of signs and numbers, tokenization and stemming, as well as a Bayesian classifier. The data used for the training of the Bayesian classifier were obtained from the Spanish Dictionary of Affect in Language (SDAL), which is a database formed by more than 2500 words manually evaluated in three affective dimensions: Pleasantness, activation and imagery, as well as same 595 words obtained following the same procedure of SDAL was used with the help of the members of the Research Center, Technology Transfer and Software Development. As a result, the software developed has 93% success in the validation tests carried out.

The Automatic Speech (ASR) area is defined as the transformation of acoustic signals into string words. This area has been being developed for many year facilitating the lives of people so it was implemented in several languages. However, the development of ASR in some languages with few database resources but with a large population speaking these languages is very low. The development of ASR in Quechua language is almost null which leads culture and population isolation from technology and information. In this work an ASR system of isolated Quechua numbers is developed where Mel-Frequency Cepstral Coefficients (MFCC), Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) methods are implemented using a database composed by recorded audio numbers from one to ten in Quechua. The recorded audios to feed the data base were uttered by natives man and women speakers of Quechua. The recognition accuracy reached in this research work was 91.1%.