# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 6 Conclusions  
Sentiment analysis research has been conducted extensively on social media data in the English language. However, a limited amount of sentiment analysis research has been conducted on social media data in the Turkish language. We created our datasets using Turkish financial tweets, and we tried five different machine learning algorithms (Neural Network, CNN, LSTM, GRU and GRU-CNN) to find sentiments on those datasets together with word embedding and pre-trained word embedding. The binary classification results were better than the multi-class classification results, as shown in Table 10.  
Our results reveal that, generally, all models perform better when they are run with pre-trained fastText word vectors. Also, binary classification results are better than multi-class classification results, as expected. Surprisingly, the results are close to each other. With pre-trained word embedding, CNN models had the best results of all. When we used word embedding, the GRU-CNN model gave better results for the binary classification and the Neural Network model gave better results for the multi-class classification. We propose a CNN model with pre-trained word embedding for binary and multi-class classifications. Its maximum testing accuracy was 83.02% and the average of its maximum testing accuracies for all folds was 78.35% for binary classifications. For multi-class classifications, its maximum testing accuracy was 72.73% and the average of its maximum testing accuracies for all folds was 65.05%. In future works, using additional layers in these models may improve their performances. The use of more specific pre-processing techniques could also improve model performances, as the collected Turkish tweets about the Turkish financial market contain many ambiguous words and phrases that make the pre-processing step difficult. In addition, enlarging the datasets could lead to better results. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Max.** & **Max.** & \\
**Model** & **Training** & **Testing** & **Average of Max Testing** \\  & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy of All Folds (\%)** \\ \hline Binary NN model with word embedding & 100.00 & 80.25 & 76.68 \\ Binary NN model with pre-trained word embedding & 100.00 & 79.32 & 76.19 \\ Multiclass NN model with word embedding & 99.57 & 63.85 & 61.59 \\ Multiclass NN model with pre-trained word embedding & 99.51 & 65.23 & 61.59 \\ Binary CNN model with word embedding & 100.00 & 77.23 & 75.82 \\ Binary CNN model with pre-trained word embedding & 100.00 & 83.02 & 78.35 \\ Multiclass CNN model with word embedding & 99.73 & 63.71 & 61.98 \\ Multiclass CNN model with pre-trained word embedding & 99.73 & 72.73 & 65.05 \\ Binary LSTM model with word embedding & 98.30 & 74.69 & 72.36 \\ Binary LSTM model with pre-trained word embedding & 100.00 & 79.32 & 75.14 \\ Multiclass LSTM model with word embedding & 99.24 & 59.52 & 58.34 \\ Multiclass LSTM model with pre-trained word embedding & 99.24 & 61.69 & 58.69 \\ Binary GRU model with word embedding & 100.00 & 80.25 & 76.93 \\ Binary GRU model with pre-trained word embedding & 100.00 & 80.31 & 77.60 \\ Multiclass GRU model with word embedding & 99.73 & 62.99 & 60.94 \\ Multiclass GRU model with pre-trained word embedding & 99.68 & 64.07 & 62.67 \\ Binary GRU-CNN model with word embedding & 100.00 & 80.56 & 76.44 \\ Binary GRU-CNN model with pre-trained word embedding & 100.00 & 80.24 & 78.47 \\ Multiclass GRU-CNN model with word embedding & 99.68 & 61.47 & 60.08 \\ Multiclass GRU-CNN model with pre-trained word embedding & 99.68 & 64.29 & 62.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparisons of model accuracies. **Author Contributions:** Conceptualization, E.M., H.A. and M.Y. ; methodology, E.M., H.A. and M.Y. ; software, E.M.; validation, E.M., H.A. and M.Y. ; formal analysis, E.M.; investigation, E.M.; data curation, E.M.; writing--original draft preparation, E.M.; writing--review and editing, E.M., H.A. and M.Y. ; visualization, E.M.; supervision, H.A. and M.Y.