# User Representation Learning for Social Networks:
## 6 Conclusions and Future Work  
In this work, we focus on learning real valued vectors for social media users by leveraging the state-of-the-art text representation methods. Important insights were gained that will be useful in developing of real-world applications that require a user representation model. The experiments show how parameters such as the representation method, hyper-parameter selection, and the type of dataset should be adjusted to achieve the best results under certain conditions. We create a one-of-its-kind dataset by harvesting social media data of manually selected 500 Twitter users to demonstrate the performance of the proposed approaches. The extensive experimental results serve as a guide for researchers from industry and academia who are interested in accommodating both textual and non-textual features in user representation tasks. A detailed comparison of the approaches covers a wide range of text representation methods including traditional methods (TFIDF), classical word embeddings (word2 vec, Glove, FastText, etc. ), doc2 vec algorithms (PV-DBOW, PV-DM), and contextualized embeddings (Elmo, Bert). Some important findings of this study that are worth mentioning are:  
1. In regards to proposed doc2 vec models: A fine-grained tweet corpus like Dataset-A and its enriched versions Datasets D and E are unlikely to be present for most of the cases. Therefore, we recommend using datasets similar to Dataset C. The performance of a model trained on Dataset C can be increased with adding more tweets from users of a specific group if there is an intended focus group. Our doc2 vec model can give 97.5% overall accuracy. PV-DM algorithm is used with min count = 5 for the best model. 2. We envision that the attempts for incorporating a wide range of social media activity data such as comment, like, retweet, mention, follow, etc. in user representation learning are at an early stage and there are still many challenges to be solved. Our model that is trained on the combination of dataset A and E can give 97.0% accuracy. 3. Although the publicly available word vectors are very simple to use and can give acceptable accuracies, they fail to perform as well as the doc2 vec approaches. In the case of using a PWE, one should consider the corpus it was trained on and the length of the vectors. Our word2 vec model trained on Dataset A can give 84.5% accuracy. The accuracy of the Glove model trained on Tweet corpus can reach to 84.0%. 4. [PERSON] and [PERSON] do not outperform the PWE models. We believe that they would perform better than the given results if they are fine-tuned or trained from scratch. We plan to conduct broader experiments using contextualized embedding methods. We also note that the following cases can also be investigated within the scope of using PWEs. However, they are not included in this study. Figure 4: TF-IDF Experiments. Download a publicly available PWE and fine-tune it for your specific task. Train a language model from scratch using the algorithm of PWE (Glove, FastText, Google News, or NumberBatch). A broad investigation of using Contextualized word embeddings in user representation models. In this study, only their pre-trained multilingual versions are used. The proposed approach has the potential to contribute to many applications within the scope of social media prediction and recommendation. Examples of these applications include the following possible scenarios:  
User similarity: Users who are most similar to a user in a network can be queried through the similarities of their embeddings. With such a system, unknown users can be defined over other similar users automatically without the need for applying manual analyses over their social media data. This approach can be useful in many social media analysis tasks such as trend analysis and reputation analysis. Social media sentiment analysis: In classical sentiment analysis, models learn from previously expressed comments with the aim of determining user opinions without considering who their authors are.