# Multimodal Sentiment Classifier Framework for Different Scene Contexts
## 5 Tests, Results, and Discussion
### Discussion
In reality, the last one is the one that is meaningful for (training) the MSC. In consequence, despite the authors' data set having a limited number of samples, there were enough to validate the planted concept. In the authors' data set, 55.66% of the samples fall in the mentioned situations, which is not an insignificant number. In summary, there are (sub-)data sets that fill the requirements to validate the paper's goals. Nevertheless, for future work, all the data sets need more samples classified by humans. In addition, the feedback of the post sentiment to the managers who receive the information to manage their companies or platforms should be the image sentiment, the text sentiment, the combined (text-image) sentiment if it exists, and the discrepancy between the image and the text sentiment (SCv = [_isc, tsc, msc, dis._]. Also related to the data set are the failures of the models, which are detected mostly in the ISC. The classification of the images where the ISC models detect positive sentiment and the image is negative or select negative and the image is positive (see Figures 7 and 8) is mostly due to the data sets not being balanced and the difficulty of having a clear classification of the \"neutral\" image. Figure 9 shows examples of images where different persons gave different classifications, going from the positive, to the neutral, to the negative. Take the example on the right; for someone who appreciates history, the knight's armor evokes a positive sentiment. For others, the complete scene might be perceived as neutral. However, for different individuals, the image might conjure thoughts of war and medieval times, leading to a negative sentiment. So, to develop a more robust model, the data set must have not only the human classification, but also the characteristics of the human classifier, and the model should also account for that information. For more information about this subject, please see [38]. Similar issues arise in the MSC due to the lack of a comprehensive data set. When evaluating both text and images together, human classifiers sometimes base their decisions on group considerations or personal biases--such as a preference for images over text, or vice versa--leading to inconsistent classifications for the same text-image pair. The solution to this problem would be to have a large number of samples for training the classifier. However, such a data set is not currently available and should be addressed in future work. All the above leads us to compare the current framework with state-of-art models. In terms of results, the present framework achieves 78.84% accuracy, which places it among the top results presented in Table 1. It is possible to say that the present framework is at the top of the results, or at least it is in line with the state-of-the-art results. However, a direct comparison is not a completely fair one for the present framework or for the models presented in the table, because (1) all use different data sets or sub-data sets; (2) some are trying to achieve the best result possible, while the present framework is proving/showing/presenting a concept/idea/goal; (3) only some models are trained with human-classified data; and (4) some models train image and text classifiers separately and then combine them, without using data where both the image and text are classified together by humans. The bottom line is, at the moment, the results in this specific area cannot be comparable until the authors from different publications use the same procedure. Nevertheless, as mentioned, it is possible to validate that the present results are completely in line with the best state-of-the-art results. Examining the results of the individual models in detail, Tables 8 and 9 present the results of the ISC and TSC individual models, and respective ensembles. It can be seen (as already discussed) that the ensembles return better results than the individual models (which was one of the research questions).