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In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records which can be used for other data analysis and data mining research. We used our secondary dataset to find and display correlations between different emotions on the videos and the correlation between emotions on the movies and users' scores on IMDb using the Pearson correlation method and found some statistically significant correlations.

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Agriculture is one of the areas whose activities depend heavily on weather forecasts. Indeed, in order to optimize their production, farmers must be able to anticipate climate conditions favorable or not to their activities by deploying the appropriate action plans. For this purpose, they consult the data daily from various suppliers of weather forecasts. However, the reliability of the forecasts of each supplier is variable according to the period, the climate or the geographical area. Farmers, therefore, have to arbitrate between suppliers daily. This paper proposes a new set of learning architecture that significantly improves the accuracy of weather short-term forecasts for the next 1-12h in order to assist farmers in decision-making.