Predicting Stock Price Changes with Earnings Call Transcripts

This paper adopts sentiment analysis approaches to predict stock price changes of 14 major U.S. airlines, using 325 earnings call transcripts from year 2007 to 2015. We combined machine learning classification techniques with Loughran and McDonald Sentiment Word Lists (Master Dictionary), and built the Python program from scratch. Text transcripts as well as stock prices were captured online. Transcripts were labeled according to sentiment scores defined by us. After a three-way data split, all six algorithms failed to result in an ideal accuracy. The results suggest that earnings call transcripts are not informative enough to predict stock price changes.Master of Science in Information Scienc

Towards Syntactic Iberian Polarity Classification

Lexicon-based methods using syntactic rules for polarity classification rely on parsers that are dependent on the language and on treebank guidelines. Thus, rules are also dependent and require adaptation, especially in multilingual scenarios. We tackle this challenge in the context of the Iberian Peninsula, releasing the first symbolic syntax-based Iberian system with rules shared across five official languages: Basque, Catalan, Galician, Portuguese and Spanish. The model is made available.Comment: 7 pages, 5 tables. Contribution to the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-2017) at EMNLP 201

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction

Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on WordNet synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas' statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value<0.01), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues

Spanish sentiment analysis in Twitter at the TASS workshop

[EN] This paper describes a support vector machine-based approach to different tasks related to sentiment analysis in Twitter for Spanish. We focus on parameter optimization of the models and the combination of several models by means of voting techniques. We evaluate the proposed approach in all the tasks that were defined in the five editions of the TASS workshop, between 2012 and 2016. TASS has become a framework for sentiment analysis tasks that are focused on the Spanish language. We describe our participation in this competition and the results achieved, and then we provide an analysis of and comparison with the best approaches of the teams who participated in all the tasks defined in the TASS workshops. To our knowledge, our results exceed those published to date in the sentiment analysis tasks of the TASS workshops.This work has been partially funded by the Spanish MINECO and FEDER founds under project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics, TIN2014-54288-C4-3-R.Pla Santamaría, F.; Hurtado Oliver, LF. (2018). Spanish sentiment analysis in Twitter at the TASS workshop. Language Resources and Evaluation. 52(2):645-672. https://doi.org/10.1007/s10579-017-9394-7S645672522Álvarez-López, T., Juncal-Martínez, J., Fernández-Gavilanes, M., Costa-Montenegro, E., González-Castaño, F.J., Cerezo-Costas, H. , & Celix-Salgado, D. (2015). GTI-gradiant at TASS 2015: A hybrid approach for sentiment analysis in Twitter. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 35–40), Alicante, Spain, September 15, 2015.Álvarez-López, T., Fernández-Gavilanes, M., García-Méndez, S., Juncal-Martínez, J., & González-Castaño, F.J. (2016). GTI at TASS 2016: Supervised approach for aspect based sentiment analysis in Twitter. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 53–57), Salamanca, Spain, September 13th, 2016.Araque, O., Corcuera, I., Román, C., Iglesias, C. A., & Sánchez-Rada, J. F. (2015). Aspect based sentiment analysis of Spanish tweets. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 29–34), Alicante, Spain, September 15, 2015.Balahur, A., & Perea-Ortega, J. M. (2013). Experiments using varying sizes and machine translated data for sentiment analysis in Twitter. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Barbosa, L., & Feng, J. (2010). Robust sentiment detection on Twitter from biased and noisy data. In Proceedings of the 23rd international conference on computational linguistics: posters, association for computational linguistics (pp. 36–44).Batista, F., & Ribeiro, R. (2012). The L2F Strategy for Sentiment Analysis and Topic Classification. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Casasola Murillo, E., & Marín Raventós, G. (2016). Evaluación de Modelos de Representación del Texto con Vectores de Dimensiónn Reducida para Análisis de Sentimiento. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 23–28), Salamanca, Spain, September 13th, 2016.Castellano, A., Cigarrán, J. & García-Serrano, A. (2012). UNED @ TASS: Using IR techniques for topic-based sentiment analysis through divergence models. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Castellanos-González, A., Cigarrán-Recuero, J. & García-Serrano, A. (2013). UNED LSI @ TASS 2013: Considerations about textual representation for IR based tweet classification. In: Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Cerón-Guzmán, J. A. (2016). JACERONG at TASS 2016: An ensemble classifier for sentiment analysis of Spanish tweets at global level. In: Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 35–39), Salamanca, Spain, September 13th, 2016.del-Hoyo-Alonso, R., Hupont, I., & Lacueva, F. (2013). Affective polarity word discovering by means of artificial general intelligence techniques. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.del-Hoyo-Alonso, R., de la Vega Rodrigalvarez-Chamorro, M., Vea-Murguía, J., & Montañes-Salas, R. M. (2015). Ensemble algorithm with syntactical tree features to improve the opinion analysis. In Proceedings of TASS 2015: workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 53–58), Alicante, Spain, September 15, 2015.Deriu, J., Gonzenbach, M., Uzdilli, F., Lucchi, A., De Luca, V., & Jaggi, M. (2016). Swisscheese at semeval-2016 task 4: Sentiment classification using an ensemble of convolutional neural networks with distant supervision. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 1124–1128), Association for Computational Linguistics, San Diego, California, http://www.aclweb.org/anthology/S16-1173 .Díaz-Galiano, M. C., & Montejo-Ráez, A. (2015). Participación de SINAI DW2Vec en TASS 2015. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 59–64), Alicante, Spain, September 15, 2015.Fernández, J., Gutiérrez, Y., Tomás, D., Gómez, J. M. & Martínez-Barco, P. (2015). Evaluating a sentiment analysis approach from a business point of view. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 93–98), Alicante, Spain, September 15, 2015.Fernández, J., Gutiérrez, Y., Gómez, J.M., Martínez-Barco, P., Montoyo A., & Muñoz, R. (2013). Sentiment analysis of Spanish Tweets using a ranking algorithm and skipgrams. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Frank, E., Hall, M. A., & Witten, I. H. (2016). The WEKA workbench. Online appendix for “Data mining: Practical machine learning tools and techniques” (4th ed.). Burlington: Morgan Kaufmann.Gamallo, P., García, M. & Fernández-Lanza, S. (2013). TASS: A Naive-Bayes strategy for sentiment analysis on Spanish tweets. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.García Cumbreras, M. Á., Martínez Cámara, E., Villena-Román, J., & García Morera, J. (2016a). TASS 2015—The evolution of the Spanish opinion mining systems. Procesamiento del Lenguaje Natural.García Cumbreras, M. Á., Villena Román, J., Martínez Cámara, E., Díaz Galiano, M. C., Martín Valdivia, M. T., & Ureña López, L. A. (2016b). Overview of TASS 2016. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 13–21), Salamanca, Spain, September 13th, 2016.García, D., & Thelwall, M. (2013). Political alignment and emotional expression in Spanish Tweets. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Hagen, M., Potthast, M., Büchner, M., & Stein, B. (2015). Webis: An ensemble for twitter sentiment detection. 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A comprehensive introduction and survey. San Rafael: Morgan & Claypool Publishers.Liu, B., Hu, M., & Cheng, J. (2005). Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th international conference on world wide web (pp. 342–351), ACM, New York, NY, USA, WWW ’05, doi: 10.1145/1060745.1060797 , http://doi.acm.org/10.1145/1060745.1060797Martínez-Cámara, E., Martín-Valdivia, M. T., Ureña-López, L. A., & Montejo-Raéz, A. (2014). Sentiment analysis in Twitter. Natural Language Engineering, 1(1), 1–28.Martínez-Cámara, E., García-Cumbreras, M.Á., Martín-Valdivia, M. T., & López, L. A. U. (2015). SINAI-EMMA: Vectores de Palabras para el Análisis de Opiniones en Twitter. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 41–46), Alicante, Spain, September 15, 2015.Martín-Wanton, T., & de Albornoz, J. C. (2012). UNED at TASS 2012: Polarity classification and trending topic system. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Martínez-Cámara, E., Ángel García-Cumbreras, M., Martín-Valdivia, M. T., & Ureña-López, L. A. (2013). SINAI-EMML: Combinación de Recursos Lingüíticos para el Análisis de la Opinión en Twitter. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Martínez-Cámara, E., Martín-Valdivia, M. T., Molina-González, M. D., & Ureña-López, L. A. (2013). Bilingual experiments on an opinion comparable corpus. In Proceedings of the 4th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 87–93).Mendizabal, I., & Carandell, J. (2015). BittenPotato: Tweet sentiment analysis by combining multiple classifiers. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 71–74), Alicante, Spain, September 15, 2015.Mohammad, S., Kiritchenko, S., & Zhu, X. (2013). Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. In Second joint conference on lexical and computational semantics (*SEM), Volume 2: Proceedings of the seventh international workshop on semantic evaluation (SemEval 2013) (pp. 321–327), Association for Computational Linguistics, Atlanta, Georgia, USA, http://www.aclweb.org/anthology/S13-2053 .Montejo-Ráez, A., & Díaz-Galiano, M. C. (2016). Participación de SINAI en TASS 2016. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 41–45), Salamanca, Spain, September 13th, 2016.Montejo-Ráez, A., Díaz-Galiano, M. C., & García-Vega, M. (2013). LSA based approach to TASS 2013. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Montejo-Ráez, A., García-Cumbreras, M., & Díaz-Galiano, M. (2014). Participación de SINAI Word2Vec en TASS 2014. In Proceedings of the TASS workshop at SEPLN 2014.Moreno-Ortiz, A., & Pérez-Hernández, C. (2012). Lexicon-based sentiment analysis of Twitter messages in Spanish. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Nakov, P., Kozareva, Z., Ritter, A., Rosenthal, S., Stoyanov, V., & Wilson, T. (2013). SemEval-2013 Task 2: Sentiment analysis in Twitter.Nakov, P., Ritter, A., Rosenthal, S., Stoyanov, V., & Sebastiani, F. (2016). SemEval-2016 Task 4: Sentiment analysis in Twitter. In Proceedings of the 10th international workshop on semantic evaluation (pp. 1–18), Association for Computational Linguistics, San Diego, California, SemEval ’16.O’Connor, B., Krieger, M., & Ahn, D. (2010). TweetMotif: Exploratory search and topic summarization for Twitter. In Cohen, W. W. & Gosling, S. 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TASS 2015 – La evolución de los sistemas de análisis de opiniones para español

El análisis de opiniones en microblogging sigue siendo una tarea de actualidad, que permite conocer la orientación de las opiniones que minuto tras minuto se publican en medios sociales en Internet. TASS es un taller de participación que tiene como finalidad promover la investigación y desarrollo de nuevos algoritmos, recursos y técnicas aplicado al análisis de opiniones en español. En este artículo se describe la cuarta edición de TASS, resumiendo las principales aportaciones de los sistemas presentados, analizando los resultados y mostrando la evolución de los mismos. Además de analizar brevemente los sistemas que se presentaron, se presenta un nuevo corpus de tweets etiquetados en el dominio político, que se desarrolló para la tarea de Análisis de Opiniones a nivel de Aspecto.Sentiment Analysis in microblogging continues to be a trendy task, which allows to understand the polarity of the opinions published in social media. TASS is a workshop whose goal is to boost the research on Sentiment Analysis in Spanish. In this paper we describe the fourth edition of TASS, showing a summary of the systems, analyzing the results to check their evolution. In addition to a brief description of the participant systems, a new corpus of tweets is presented, compiled for the Sentiment Analysis at Aspect Level task.This work has been partially supported by a grant from the Fondo Europeo de Desarrollo Regional (FEDER), REDES project (TIN2015-65136-C2-1-R) and Ciudad2020 (INNPRONTA IPT-20111006) from the Spanish Government

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets