Linked Data-based Social Media Analysis for Stock Market Tracking

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1 Linked Data-based Social Media Analysis for Stock Market Tracking Priyanka Dank University of Bonn Bonn NRW, Germany Simon Scerri Fraunhofer IAIS Schloss Birlinghoven Sankt Augustin NRW, Germany Ali Khalili Dept. of Computer Science VU University Amsterdam De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands Abstract The rising demand of social media and ensuing activities provide a rich data source for opinion mining. The efforts reported in this paper analyze a popular micro-blogging social network for one of the important application domains, i.e., stock market prediction. The novel approach developed includes the use of the Semantic Web technologies to more accurately interpret social media streams. The algorithm closely examines the visible public mood vis-a-vis an organization and related entities, namely products and affiliated persons, and considers the effect of these parameters in calculating the sentiment. The entities and their relationships to the organization of interest are retrieved from one of the largest Linked Data repository available online. Furthermore, we assess the algorithm by collecting the public sentiments as well as the intra-day stock prices during our experiments followed by the statistical tests on the collected data. The evaluation takes into account different company profiles according to our heuristics to study any detected correlation in detail. The exercise is repeated with the benchmark method, which disregards the two additional entities we consider. The evaluation considers whether the fluctuating sentiment is reflected in stock prices, and the most suitable time-lag. I. INTRODUCTION Seminal theories of stock market prediction suggest that stock price fluctuations are principally governed by a random walk hypothesis, i.e., movements cannot be predicted [2], [3], [4]. However, a large number of recent studies have shown that prediction through social media content analysis is, to an extent or another, possible [1], [14], [24]. Social media has become an important tool in application areas concerned with shifting public opinions. Most opinion mining and sentiment analysis methods involve the use of some level of natural language processing in order to determine the polarity of the text, which is then attached as a label (e.g., positive, negative or neutral) or a number within a range [19]. In this paper, we also investigate whether social media can be used to predict stock market movements. To try achieving more significant results than related efforts, we introduce a novel method that exploits the existence of domain Linked Open Data (LOD) in order to more accurately gauge public online sentiment related to a primary entity (i.e., a company, organisation or enterprise). We also utilise social media streams from one of the most popular platforms, Twitter 1. In order to carry out this investigation, we apply a standard sentiment analysis method to the Twitter data stream to calculate and track changes in the online sentiment. Due to the concise and informal nature of tweets, sentiment analysis is known to be more challenging than when applied on other forms of text content [9]. We opt for an existent sentiment analysis method to determine public online sentiment at regular intervals, and identify whether any correlation exists between the resulting fluctuations and the actual stock markets for a given company. The decision to perform correlation tests lies in the assumption that public trust in a company, as well as (or based on) its products and key people, is the lurking variable or common factor between both i) changes in sentiment expression in the social media and ii) stock market values, related to the company. Besides correlation tests for concurrent data available for both variables, we also perform these tests repeatedly with different time-lags, to identify the most reliable time lapse between online sentiment expression and any corresponding trends in the stock market, given any correlation is identified. Furthermore, we advance on the state-of-the-art by attempting to improve the method for determining periodic sentiment for a given entity. Specifically, we investigate a novel method that does not only gauge sentiment surrounding a primary entity, but also considers secondary entities (i.e., a company s products and key figures or personnel) that form part of an extended context. We thus refer to a benchmark and our novel alternative approach: Benchmark (Keyword-based): Sentiment Analysis is applied on tweets which directly mention the primary entity. Alternative (Context expansion): Sentiment Analysis is applied on tweets mentioning the primary entity (direct), associated entities (indirect), or a combination of. To enable this alternative approach, we first perform context expansion to return a list of secondary entities related to a primary entry. Context expansion is achieved by retrieving entities that i) conform to a specific semantic type and ii) 1 Current records indicate over 284 million monthly active users generating over 500 million tweets per day.

2 are semantically linked to the primary entity with a specific relationship. The results are obtained by executing pre-defined SPARQL queries over DBpedia 2. In the second step, the sentiment analysis task is then performed on the tweets with extended results collectively, based on a system of weights that determine the relevance of the primary and secondary entities on the overall score. To evaluate the effectiveness of both benchmark and alternative methods, correlation tests are then performed over data collected over the course of at least 1 week, and the results presented and contrasted. Before providing the details of the two approaches in Section 3, and the results of the correlation tests in Section 4, in Section 2 we report on related efforts in the area. After concluding in Section 5, we also suggest future avenues for this research. II. RELATED WORK Studies performed over the last decade have indicated that, rather than following a random walk, stock market changes are influenced by public sentiment [17]. In particular, strong correlations have been identified [24] between hope, fear and worry indices expressed in Twitter posts and stock market indicators such as DJIA (Dow Jones Industrial Average), the National Association of Securities Dealers Automated Quotations (NASDAQ) and Standard & Poor s(s&p). In particular, the researchers behind the same experiment observed that emotional outbursts in the social media provide for a reliable predictor of stock market levels in the following day. Additional studies [1], [5] have similarly concluded that financial decisions can be linked to the universal public mood, and that therefore, stock market movements can be predicted by monitoring public mood swings in the social media. Other efforts developed algorithms that combine social media sentiment analysis with recent stock prices in order to predict stock market movements [13]. Our approach is also based on the assumption that public opinion reflects or even precedes stock market movements for an enlisted company, and we also rely on social media as an instant reflection of public mood worldwide. Regardless of the affirmative indications reported in previous work, the first experiments performed in the evaluation of our approach also investigate the presence and extent of the correlation between the two variables. A large number of related efforts also consider entitydependant approaches to social media-based stock market prediction. The approach described in [6] retrieves tweets based on a primary keyword. Target-dependent features are then identified by considering the grammatical structure and syntax of the sentence. Alternatively, efforts such as [22] rely on the selection of one of the S&P500 stock symbols as the entity of interest when retrieving tweets for analysis. The efforts presented in [15] also follow this approach but go one step further by collecting data from other data sources in addition to Twitter, including 11 online message boards such as Yahoo! Finance as well as various news articles. Other approaches 2 apply active learning by training classifiers based on manual, company-focussed labelling of tweets [23] to consider the collective impact of tweets on a specific company, its products and its stock value. However, our approach differs since none of the above-discussed approaches consider secondary entities that are closely related to the company of interest when retrieving social media posts on which to perform the sentiment analysis. Sentiment analysis and opinion mining are extensive research fields in their own right, and in this paper we do not seek to compare the state-of-the-art in the area. Instead, we adopt an existing method which we deem most suitable for our approach. However, we compare the way we chose to apply the chosen method to other approaches. Most of the more advanced efforts consider the detection of key words or phrases expressing an opinion in the vicinity of a respective entity, rather than in the entire text [11], [20]. This is even more crucial when considering the brevity of social media posts, which tend to be rather short (or very short, in the case of tweets, which are also known as microposts). Thus, we also consider sentiment around identified entities of interest. To calculate such a sentiment score, efforts such as [8] consider different lengths of sentiment regions, or token windows. We similarly consider a token window of ±3. The application of ontologies or Linked Data for this usecase is not novel. Efforts like [9] describe ontology-based techniques to calculate individual and more reliable sentiment scores for each distinct entity in a post. The authors of [21] describe an approach that adds semantic concepts as additional features in training sets for sentiment analysis. An evaluation concludes that semantic features produce a better F-score only when considering negative sentiment. Our context expansion proposal is similar to semantic query expansion, whereby an initial query related to a keyword or entity is expanded to cover additional entities. This method has been investigated for different use-cases. In [18], for example, lexico-semantic expansion is used to enhance Twitter-based event detection. Semantic relationships between hashtags, inferred from correlation statistics, are investigated to expand the original hashtags and retrieve further tweets. In contrast to this kind of approach, we do not need to manually create or infer new concept maps in order to perform our company-centric entity set extraction. Instead, we rely on up-to-date open data provided by the international community. Specifically, we take advantage of the vast information available in the DBpedia knowledge repository, a machine-processable interpretation of Wikipedia and a major node in the LOD Cloud. Related efforts such as [10] have successfully used DBPedia-based semantic networks and spreading activation methods to generate more extended and more accurate user interest profiles. Similarly, we use DBPedia to derive ontological relationships between a company and its products and key personnel. The reliance on a LOD repository also guarantees that we do not need to manually update the concept maps when facts change (e.g. key personnel change, new products are released), since in Wikipedia and subsequently DBpedia this tends to be done

3 quite instantly by individuals with an interest or stake in the described concepts. III. L INKED DATA - BASED C OMPANY S ENTIMENT T RACKING Fig. 1 illustrates the implemented company sentiment tracking process, which is primarily composed of three main steps: 1) Linked Data-based Context Expansion 2) Linked Data-based Stream Processing 3) Periodic Sentiment Approximation The methodology behind the above stages is described in detail in the sections below. A reference implementation of the approach has been developed as a plugin to our Realtime Semantic Analyzer (ReSA3 ). ReSA is an extension of the context platform[7], which fetches real-time tweets referring to an entity of interest (a string) after this is resolved to a DBpedia entity using the DBpedia Spotlight [12]. Fig. 2 shows a screenshot of the ReSA SentiTracker plugin4 in use. Fig. 2. ReSA SentiTrack in use. company in question is not. In addition, a Tweet containing two or more relevant entities can be considered to be more representative when approximating public sentiment. After a company is matched to a DBpedia representation, the URI is used to retrieve secondary entities by executing a pre-defined set of SPARQL queries over the same repository (using the sparql-client NodeJS packagehttps:// and the DBpedia SPARQL endpoint5 ). The retrieval is not straightforward due to the lack of structure and consistency exhibited by DBpedia. For example, although at times key linked people were available as instances of foaf:person, in other cases they were merely expressed as a string. In addition, a number of DBpedia properties are more or less arbitrarily used to link a company with its products and key people. Following a domain-dependent survey of DBpedia content, the list of properties shown in Table I was identified. These were then embedded in a pre-defined series of nested SPARQL queries to obtain the two types of relevant entities. B. Linked Data-based Stream Processing Fig. 1. Process Flow Diagram A. Linked Data-based Context Expansion The decision to expand a primary entity of interest (a company) into a set of entities is based on the assumption that the strategy planning done by key personnel affiliated with that company, as well as the quality and reliability of the products and services it offers, influence public opinion about the company, and can ultimately also affect its stocks. Thus, for our objective it is also worth to consider microposts mentioning products and/or key personnel, even when the The set of entities returned by the SPARQL queries are stored and their labels are fed to the Twitter Streaming API for filtering purposes. Public tweets referring to any number of these labels are retrieved and subjected to a number of operations, as outlined in the following subsections. 1) Entity Recognition and Annotation: In this step, DBpedia Spotlight is invoked for automatically annotating mentions of DBpedia resources in the micropost text. Before applying the Named Entity Recogntion (NER) tool, we use a language detector tool to detect the language of the tweet. Based on the detected language, we dispatch the NER task to the right DBpedia Spotlight instance tailored to that specific language. This will improve the quality of NER by employing better indexes for natural language processing and entity spotting. The output of NER task will be a set of quads consisting of a URI, the matching surface form, the entity type and the Try it out here: 5

4 Products dbpedia.org/ontology/developer dbpedia.org/ontology/manufacturer dbpedia.org/ontology/designer dbpedia.org/ontology/builder dbpedia.org/ontology/product dbpedia.org/ontology/owner dbpedia.org/property/parent dbpedia.org/property/parentcompany dbpedia.org/property/constructors dbpedia.org/property/engine dbpedia.org/property/manufacturer dbpedia.org/property/manufacturer dbpedia.org/property/developer dbpedia.org/property/owner dbpedia.org/property/currentowner Key People dbpedia.org/ontology/keyperson dbpedia.org/ontology/occupation dbpedia.org/ontology/board dbpedia.org/ontology/employer dbpedia.org/ontology/knownfor dbpedia.org/ontology/division dbpedia.org/ontology/institution dbpedia.org/ontology/foundedby dbpedia.org/property/keypeople dbpedia.org/property/ceo dbpedia.org/property/editor dbpedia.org/property/chiefeditor dbpedia.org/property/publisher dbpedia.org/property/employer dbpedia.org/property/team dbpedia.org/property/teams dbpedia.org/property/sbkmanufacturers dbpedia.org/property/office dbpedia.org/property/workinstitutions dbpedia.org/property/title TABLE I PROPERTIES EMBEDDED IN THE SPARQL QUERIES offset in the given text. Amongst the returned results, the ones matching the entities of interest are instantly identified by matching the returned URI to the ones returned by the SPARQL queries. Spotlight, however, is not fully adequate to tackle our requirements. First, initial trials indicated that Spotlight can on occasion not identify all matching surface forms. For this reason, we implemented a second procedure to ensure that all occurrences of SPARQL-retrieved entities in the text are recognised and annotated. Second, for our sentiment analysis task we do not require all identified DBpedia entities, but only the ones in the expanded context. Therefore, entities which do not match are discarded by employing the Ask SPARQL query. A post-filtering step is then executed to exclude any resulting tweets which have no relevant entities annotated. Table II illustrates an example of results obtained following this step, showing the identified entity URIs and their known type(s). Company Filtered Tweets Entity Inc. 10 Quotes That [DBpedia:Agent, Schema:Organization, Will Make You DBpedia:Organisation, Apple Love Apple CEO DBpedia:Company] ; Tim Cook $AAPL Cook [DBpedia:Agent, Schema:Person, Foaf:Person, DBpedia:Person]. Google accidentally leaked hundreds of thousands of [DBpedia:Company, Google customers personal DBpedia:Organisation, details and didn t DBpedia:Agent, Schema:Organization]. notice for 2 years Microsoft in positive Microsoft talks with all publishers for Xbox One s backward compatibility. com/1e9cuxy [DBpedia:Agent, Schema:Organization, DBpedia:Organisation, DBpedia:Company] ; one [Schema:CreativeWork, DBpedia:Work, DBpedia:Software]. TABLE II ANNOTATED TWEET EXAMPLES 2) Entity-based Weight Assignment: The expansion of one initial interesting entity into a set which includes secondary entities introduced the need to consider different impacts the presence of entity types has on the computed tweet sentiment score. For the purpose, we devised a series of interest profiles with varying entity-type weights. For our main use-case, we noted that for some kinds of companies, tweets expressing public sentiment tend to be more product-oriented, whereas others were more personnel-oriented. Based on this observation, we devised the heuristic shown in Table III. All three shown profiles consider company references as the most influential (half of the total weight), but differ with respect to product and personnel weights. If the company is unknown at execution stage, profile A is assigned by default. The assigned weights are used in conjunction with the computed sentiment scores in the weighted sentiment score calculation step which is explained in section III-B4. In direct comparison to this weighting scheme, the benchmark method (which only considers the presence of company references) would only have a fixed weight of 0.5 for each company reference. However, in order to determine whether factoring-in additional related entities improves sentiment tracking, in the evaluation no such weight is established for the benchmark, i.e., a neutral weight of 1 is assumed for each company reference. Profile Fitting Company Examples Company Product Person Weight Weight Weight Company A Apple Inc., Google Company B BMW, Unilever Company C Goldman Sachs, Morgan Stanley TABLE III ENTITY-BASED WEIGHTS 3) Entity-centric Sentiment Calculation: Our sentiment calculation follows a Lexicon-based approach, using the AFINN- 111 wordlist which contains 2477 manually-rated English words and phrases[16]. We employ the NodeJS Sentiment module 6, which uses the above wordlist to assign scores to a given text. However, the end-score assigned by this module is a cumulative score of the identified words in the entire text. Since we want to consider the sentiment around detected interesting entities individually, this was not an ideal solution. Therefore, we only use the Sentiment module to identify positive and negative matches against the AFINN- 111 wordlist. These are assigned scores of ±1. We then calculate sentiment around each relevant entity by considering the sentiment lexicons present in its immediate vicinity. Given the 140-character limitation of Tweets, looking at the 3 tokens ahead and after the entity is considered appropriate. The entitycentric score is calculated by summing up the scores of any identified sentiment lexicons in this range. In addition, following the weight assignment strategy introduced in Table III we respectively sum up company, product 6

5 and person sentiment scores so that each micropost analysis results in three sentiment scores (one for each entity-type). Table IV shows sample results for the tweets in Table II, including identified relevant entities and entries in the sentiment lexicon. No. Filtered Tweets cs prs kps #1 #2 #3 10 Quotes That Will Make You Love + Apple CEO Tim Cook $AAPL Google accidentally leaked hundreds of thousands of customers personal details and didn t notice for 2 years Microsoft in positive + talks with all publishers for Xbox One s backward compatibility. TABLE IV TWEET SENTIMENT SCORES ) Weighted Sentiment Score Calculation: In this stage, the assigned entity-based weight is combined with the entitycentric sentiment score to obtain a weighted tweet sentiment score, t w s. The calculation is expressed by Equation 1: t ws = ( c s c w ) + ( pr s pr w ) + ( kp s kp w ) (1) where c s, pr s and kp s are respectively the individual sentiment scores calculated for each company, product(s) and key person(s) referenced in the text, and c w, pr w and kp w are the profile-defined company, product and key personnel weights. Table V shows the result for the three example tweets based on the intermediary results shown in Table IV and the relevant weight profile introduced in Table III: No. Profile Entity-type Sentiment Entity-type Weights cs prs kps c w pr w kp w t ws #1 A #2 A #3 A TABLE V WEIGHTED TWEET SENTIMENT CALCULATION C. Periodic Sentiment Approximation The method so far is concerned with calculating sentiment for individual tweets in continuous social media streams. The next decision was to consider how the resulting stream of sentiment scores can be interpreted to approximate general public sentiment expressed about an entity in more discrete time periods. This is required to enable comparative studies with other information streams, such as stock levels for our stock market tracking ambitions. We approach the above requirement by calculating a moving average that is representative of general expressed sentiment in a specific time period. Equation 2 defines the dynamic moving average sentiment score S p calculated during a time period p. When the time period is elapsed, the final S p value represents the average detected sentiment in that time period. Tws S p = (2) T where T is the total number of (relevant) tweets for which a sentiment score is calculate within time period p and Tws is the sum of these scores in the same period 7. In order to smoothen out the evolving S p values and ensure a value in the range of ±1, we also introduce the following normalisation function for n(s p ): ifs p > 0 : n(s p ) = else : n(s p ) = ( 1 ( 1 1 ) 1 (3) 1 + S ) p 1 (4) S p In order to demonstrate the behaviour of the process and described functions, we extend the examples shown to obtain the three first values of the normalised moving average sentiment n(s p ) for the three tweets shown in the running example 8 Total Tweets T Incoming Tweet t ws Total Sentiment Tws Moving Average S p Normalised n(s p) TABLE VI MOVING AVERAGE SENTIMENT CALCULATION IV. EXPERIMENTS AND DISCUSSION The main hypothesis behind our contributions, in the context of the selected use-case, is that social media could reflect or even predict stock market movements. To evaluate our proposed method and investigate this hypothesis, we establish the following three research questions: 1) Can any correlation between periodical sentiment approximations and corresponding stock movements be identified? As discussed in the introduction, one can interpret positive correlation as an indication that both expressed social media sentiment and stock market values are influenced by a third common factor: public trust in a company (and its key people and products). 2) If a positive correlation is identified, can social media be used to predict stock movements, i.e., does the expressed general sentiment precede stock changes, and 7 By definition a moving average evolves indefinitely. However, periodical sentiment scores are easier to define and work with. In particular, for the usecase targeted in this paper it is more useful to compare an average sentiment score that is calculated on an hourly, daily, weekly, etc., basis. 8 In reality the consideration of these tweets in conjunction is not valuable for our use-case, since they refer to different companies. However, we stick to the same examples for the sake of simplicity

6 for which time interval is the observed correlation most significant? 3) Does our context-expansion approach (company, products, key persons) offer any observable value over the benchmark approach (company only)? A. Experimental Setup To answer the above questions, we organised the following experimental setup. The implemented approach was deployed on two workstations, which were configured to consume, filter and classify live tweets; as well as stock market movements, over a week. We considered 6 companies corresponding to the different company profiles in Table III. As per Equations 2 and 3, we then set a time period of 1 week, and executed the processing only during those hours in which the stock markets are active. To address the first question, we then submit the two corresponding value pairs to correlation tests. To address the second question, we perform these tests repeatedly with different timelags to identify the most promising time lapse between online sentiment expression and any corresponding trends in the stock market. Besides the default realtime comparison (lag 0, executed every 60 minutes on the hour), we submit pairs of values for correlation tests with incremental hourly lags (lag of 1 to 12 hours). To address the third question, the above experiment was parallelised to execute both Benchmark and Alternative approaches at the same time, so as to enable a direct comparison. In addition to the above basic setup and procedure, we introduced a second experiment. In this variant, the number of tweets was reset every 24-hours. This arrangement translates to a fluctuating moving average sentiment score calculated on a daily basis, rather than having a continuously calculated score over the entire span of one week. This means that the relevance of older sentiment scores expires at a steeper rate in favour of recent ones. In addition, in this variant products and/or key personnel are only factored-in when either of them is present. In terms of the chosen weighting system, it means that if no product or person was recognised in a relevant tweet, the company weight was raised to a full 100%. Thus, only when other entity types are detected is the relevance of company-centric sentiment reduced to also account for sentiment detected around secondary entities. This last condition only applies to the alternative approach, since the benchmark approach only considers one primary entity. The introduction of this variant produces four different results, which we will below refer to as Benchmark-Basic and Alternative-Basic and their variants Benchmark-Var and Alternative-Var. B. Results In this section, we interpret and discuss the data collected in the week-long experiment. To illustrate the data collected for each of the chosen 6 companies, Fig 3 demonstrates a sample (37 observations) of the results collected over the entire period for one company: BMW. The stock market movements observed are shown (values on the right y-axis) together with the four calculated moving average sentiments based on the four methods under consideration (values on the left y-axis). From the plot, it can instantly be observed that the decision to reset the number of tweets when calculating the moving average sentiment in the variants produces a behaviour that is less smooth (dashed and dotted lines) than their basic counterparts (solid lines). Fig. 3. BMW Moving Average Sentiment scores against Stock Market values for Company Benchmark Alternative Basic Var Basic Var Apple Inc Google Average (Profile I) BMW UniLever Average (Profile II) Morgan Stanley Goldman Sachs Average (Profile III) AVERAGE MEDIAN TABLE VII INSTANT CORRELATION RESULTS (NO TIME LAPSE) FOR DIFFERENT EXPERIMENTS Another visually observable result is the indication that there could indeed be some degree of correlation between the social media-based scores and the stock market movements. To examine this possibility, we submitted the computed moving average sentiment values (for each of the four described methods) and the corresponding observable stock market values to correlation tests. The experiment was repeated for the six identified companies and the 24 distinct results are shown in Table VII. In order to interpret the results collectively, averages are also calculated for each of the three company profiles (intermediate rows) and for all six companies collectively (last row). The latter is supplemented by the median. By observing these values we can make the following conclusions: 1) On average, there is low to moderate correlation (-1 < < 1) between the sentiment scores and

7 the stock values. The average result is not completely random (supported by the tests for the median), and only one of the six companies exhibits a negative correlation (Unilever). 2) The results suggest that the basic method performs generally better than the variant, for both benchmark and alternative. 3) The results suggest that our proposed alternative method does not perform significantly better or worse than the benchmark. A two-tailed t-test with a 0.05 level of significance fails with a P-Value of The correlation values between the two variables in Table VII were computed at the end of each hour in the testing period. The next series of experiments sought to investigate whether higher correlation levels are observed when the two variables are not considered at the same instant. The main purpose of this experiment is to determine which time interval is most suitable for predicting market movements from the expressed online sentiment. Fig. 4 shows the correlation coefficients calculated for each of the six selected companies when shifting the comparisons by 12 incremental hourly intervals. The first value shown in each graph (time lag = 0) is equivalent to the results shown in Table VII. Similarly, the correlation results are produced for all four alternative methods. Fig. 4. Correlation values for 12 different time intervals From these graphs, it can be observed that there is no general rule for determining the best time interval. In three of the experiments, the basic benchmark and alternative methods perform best instantly (Google, BMW, Goldman Sachs). In the other three, higher correlations are observed using time intervals 6 (Unilever) and 12 (Apple Inc., Morgan Stanley). Considering only the two basic methods (benchmark-basic, alternative-basic), the averages for the 6 result sets in Fig. 4 were calculated to interpret the results further. The result, shown in Fig. 5, suggests that instant correlations tend to be stronger. Fig. 5. Average correlation values for 12 different time intervals The above results provide a systematic answer to all three targeted questions. Although larger-scale experiments are required to confirm the first question, based on our experiments there is a strong indication that there is a common factor influencing both social media sentiment and stock market values related to a company. Although longer experiments can be organised and planned as future work, the data provided by the public Twitter API might not be sufficient to provide the required data test bed. Our answer to the second question is that, if it is possible to predict stock market movements based on sentiment expressed in social media, the prediction is more or less instant and the likelihood of a correct prediction starts to drop sharply if interpreted within an hour s timeframe, or longer. The answer to the third question, which is also the main hypothesis targeted in this paper, proved somewhat disappointing. Although our alternative method is more complex than the benchmark, and also considers tweets that refer to the secondary object of interest even when the primary one is unmentioned, the same correlation levels are observed. Following an in-depth manual investigations of the results, we observed that although our extended method is of value, the majority of tweets retrieved when performing the sentiment scoring refer to the primary object of interest, and thus the effect of the other tweets remains negligible. Secondly, we also observed that the biggest value of our linked data-based method relies on the proper identification of multiple entities of interest in one tweet, and the success rate of the sentiment analysis. Unfortunately, the two existing libraries we used for entity recognition and sentiment analysis do not always perform satisfactorily. For example, although the tweet Google Maps for Android got a smashing new feature

8 that will make iphone users jealous should be recognised and rated as positive based on our alternative method (when considering Google ), it fails both our filtering stage and the sentiment scoring. DBpedia Spotlight fails to recognise Android ; and in general the library has been observed to omit recognition of some entities seemingly randomly. In addition, although the positively-connotated word smashing is within the region of the recongised company product, it is not in the selected wordlist used by the selected sentiment module. V. CONCLUSIONS AND FUTURE WORK The presented novel method monitors sentiment expressed in the Social Media with the intent of predicting stock market movements. It advances contemporary studies by performing context expansion based on a primary entity and semantic knowledge extracted from LOD. In our implementation, we relied on Twitter and DBPedia respectively as the social media stream and knowledge repository of choice. Linked Data-based context expansion replaces the need to manually create and maintain concept maps when monitoring a single main entity of interest is not sufficient. In our use-case, we simply adopt existing ontological relationships that link a company to its products and key personnel. We investigated different methods for monitoring evolving public sentiment for a chosen company, ahead of comparing the results against its intra-day stock prices. The two variables were subjected to a series of correlation tests to identify the potential and most suitable time interval for stock market prediction. At the same time, we also compared our context expansion method against the company-centric benchmark. The results indicate low to moderate correlation, confirming that social media analysis can contribute to stock market prediction, especially when interpreted within a 1-hour time frame. Although our alternative method did not offer any immediate advantage, the analysis of individual results indicates a higher potential when used to track companies whose products or key personnel are also highly and individually discussed. The implemented tracker, which is available online, is also provided as open-source, enabling extension by the interested community. We will strive to improve the implemented method in three main ways. Through the adoption or development of a sentiment analysis module that is more suited for the stock market domain we attempt to improve entity-centric scoring. The selection of LOD as a vessel for semantic expansion also has its limitations. For example, DBpedia has an error rate of 5-10%. A lot of efforts continue to invest significant effort to improve data curation technology and guarantee the veracity of these datasets. Being at their forefront through projects such as LOD2 9 and Diachron 10, we will immediately apply the most recent best practices. We will also extend social media stream coverage to included other social networks such as Facebook and LinkedIn, as well as reliable product review fora ACKNOWLEDGEMENT We thank the CEOs at Stockpulse GmbH; Mr. Stefan Nann & Mr. Jonas Krauss for their collaboration in our efforts. REFERENCES [1] Johan Bollen, Huina Mao, and Xiaojun Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1):1 8, [2] Maria Rosa Borges. Efficient market hypothesis in european stock markets. The European Journal of Finance, 16(7): , [3] Paul H Cootner. The random character of stock market prices [4] Eugene F Fama. Efficient capital markets: Ii. The journal of finance, 46(5): , [5] Eric Gilbert and Karrie Karahalios. Widespread worry and the stock market. In ICWSM, pages 59 65, [6] Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 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