# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 4 Results
### Topic Modeling Performances and Results  
To perform the task of extracting the most discussed topics, we have evaluated the T topics with \(c\_v\) maximum in each LDA model described in Section 3.4. As a result,  
Figure 5: Frequency of words for both Positive and Negative categories. On the right, most frequent words for Positive (Top Positive) and Negative (Top Negative), and both (Characteristic).  
we decided to select the model shown in Figure 6. This LDA model uses lemmatization, stemming, and bigrams with a minimum frequency to create them to 10 times.  
From the previous figure, it is observed that the maximum value of coherence score (_c_v_) is located in 7 topics with a value of 0.4956. The principal reason for selecting this model among the rest of the models analyzed during the project involves the above-mentioned combination between high _c_v_ results and human interpretability. In our particular case, we experimented that some of the models described in Section 3.4 had higher values of _c_v_ score than others. However, after the individual analysis of each topic (particularly the words this topic contains) from those models, we experimented that some of them could be grouped as the same topic.  
For this reason, in order to ensure that each topic represents a unique theme in the dataset, we combined the previous _c_v_ analysis from Figure 6 with a pyLDAvis [31] implementation (Section 3.4). As a result, Figure 7 shows the distance between topics in a two-dimensional space using pyLDAvis [31].  
Figure 6: LDA model selected for its analysis.  
Figure 7: Topic modeling representation.  
We can notice that all the topic groups from Figure 7 are well separated from each other. For this reason, topics do not have similarities between them, and we can ensure that our topic modeling proposed is viable for its analysis.  
After this previous analysis, we have determined that the optimal number of topics in the dataset refers to seven non-overlapped topics. Intending to optimize the solution created in this section, we implemented some of the most widely used topic models. Specifically, this work has focused on the comparison between the Non-negative matrix factorization (NMF) [42], the Latent Semantic Analysis (LSI) [43], and the LDA models, taking advantage of the OCTIS [44] project. This project help researchers easily train, analyze and compare several Topic Models on their corpus. OCTIS optimal hyperparameters can be estimated by means of a Bayesian Optimization approach. In this work, we have used OCTIS to compare the topic models mentioned above through several evaluation metrics (coherence score and topic diversity).  
Table 2 shows that the models that use NMF and LDA for topic modeling lead to the best performance metrics. As they both have similar results, we decided to continue focusing on the model with the maximum \(c\_v\) score. For this reason, we selected the model that uses the LDA method for distributing the words from the corpus into the seven topics in order to analyze them individually.  
Before giving a name to each of the seven topics obtained, previous research on transport platforms and customers services was realized. As a result, Table 3 shows these seven most discussed topics in the dataset with its most frequent words providing a word cloud, a detailed description of each of the topics, and the name of the topic.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Topic Modeling Evaluation**} \\ \hline
**Evaluation Metrics** & **NMF** & **LDA** & **LSI** \\ \hline C\_V & 0.4884 & 0.4902 & 0.3820 \\ \hline UMass & \(-2.5915\) & \(-2.727\) & \(-2.2890\) \\ \hline UCI & 0.1641 & 0.1417 & 0.0039 \\ \hline NPMI & 0.03629 & 0.03175 & 0.00496 \\ \hline Topic Diversity & 0.6857 & 0.8142 & 0.3714 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Topic models evaluation for T = 7 topics.