Venue: NAACL 2018

# Benchmarks and models for entity-oriented polarity detection

Lidia Pivovarova, Arto Klami, Roman Yangarber

University of Helsinki, Finland

first.last@cs.helisinki.fi

###### Abstract

We address the problem of determining entity-oriented polarity in business news. This can be viewed as classifying the polarity of the sentiment expressed toward a given mention of a company in a news article. We present a complete, end-to-end approach to the problem. We introduce a new _dataset_ of over 17,000 manually labeled documents, which is substantially larger than any currently available resources. We propose a _benchmark_ solution based on convolutional neural networks for classifying entity-oriented polarity. Although our dataset is much larger than those currently available, it is small on the scale of datasets commonly used for training robust neural network models. To compensate for this, we use _transfer learning_--pre-train the model on a much larger dataset, annotated for a related but different classification task, in order to learn a good representation for business text, and then fine-tune it on the smaller polarity dataset.

## 1 Introduction

We report on research done in the context of PULS--a project for monitoring business news media Du et al. (2016); Huttunen et al. (2013).1 The system gathers 8,000-10,000 documents daily; each document is processed by a cascade of classifiers, including a named entity (NE) recognizer. A key NE in business news type is _company_ or _organization_, which can be mentioned in a positive or negative context. For example, launching a new product or signing a new contract is viewed as a positive event; involvement in a product recall, bankruptcy or fraud is considered negative.

Footnote 1: [http://newsweb.cs.helisinki.fi](http://newsweb.cs.helisinki.fi) or [http://puls.cs.helisinki.fi](http://puls.cs.helisinki.fi)

We focus on determining the _polarity_ of a mention of a given company in news media. Polarity classification is important, since if a company appears in negative contexts frequently, it may affect its reputation, impact its stock price, etc. Polarity prediction, as defined here, is similar to sentiment analysis Liu and Zhang (2012): both require the system to classify a span of text as positive or negative. However, there are crucial differences. Business news articles typically do not aim to express emotion or subjectivity--positive and negative events are usually described in a neutral tone. Thus, vocabularies of _affective terms_--e.g., _amazing_ or _terrific_--commonly used in sentiment analysis, are not helpful for business polarity. Analysis should rather focus on _affective events_Ding and Riloff (2016), i.e., stereotypically positive or negative events. Further, business news employs genre-specific word usage; words seen as negative in "generic" contexts, may indicate a positive context here, and _vice versa_.Negative terms in Hu and Liu (2004), e.g., include _"cancer"_, which in business often appears in positive contexts, as when a pharmaceuticals unveil novel treatments.

While most work in sentiment analysis is done at the document level, we aim to classify _entity mentions_ in text. This requires changes to document-based classification models. We explore two convolutional neural network (CNN) architectures, initially proposed for _document-level_ classification, and adapt them for entity-oriented classification. The modified models have an additional input channel: the _focus_--the position(s) in text where a target company is mentioned. Focus helps the model distinguish among different companies mentioned in text and assign them polarity independently.

As far as we aware no suitable datasets exist for training models for entity-oriented polarity classification. We annotated a dataset of over 17,000 business news articles, which we release for public use, to provide a foundation for an eventual standard evaluation. Despite being much larger than any existing datasets for business polarity detection, it is still small compared to what is typically

used when training CNNs for text classification.

We attempt to compensate for the small training data by transferring knowledge from a different corpus. The second corpus is large, but annotated for a different task: each document has a set of _event_ labels; _some_ of these may be mapped to polarity labels. We explore two strategies for knowledge transfer: i) manually mapping from event labels to polarity labels, and ii) pre-training CNNs for the event classification task, followed by unsupervised transfer of high-level features from event classification to polarity. We demonstrate that unsupervised transfer improves performance.

## 2 Related work

**Sentiment analysis:** Deep learning for sentiment analysis is an active area of research. Some methods learn vector representations for entire phrases dos Santos and Gatti (2014); Socher et al. (2011); others learn syntactic tree structures Tai et al. (2015); Socher et al. (2013). A simpler approach using CNNs Kim (2014) has demonstrated state-of-the-art performance Tai et al. (2015).

Interest in applying sentiment mining to the business domain is spurred by important industry applications, such as analyzing the impact of news on financial markets Ahmad et al. (2016); Van de Kauter et al. (2015); Loughran and McDonald (2011). If a company frequently appears in news in negative contexts it may affect its reputation, impact its stock price, etc., Saggion and Funk (2009). Although news reports usually have a time lag, events reported in news have longer-term impact on investor sentiment and attitudes toward a given company Boudoukh et al. (2013).

A major difficulty in training entity-oriented polarity models is the lack of publicly available datasets. In the corpus of 5,000 sentences published by Takala et al. (2014), most instances (sentences) contain no company name, and hence cannot be used for predicting polarity for specific entities. A dataset of 679 sentences in Dutch, annotated with entity-oriented business sentiment, was published by Van de Kauter et al. (2015). They demonstrate that a. in financial news, not all sentiment expressions within a sentence relate to the target company; b. sentiment is often expressed implicitly.

A shared task on fine-grained sentiment analysis of financial microblogs and news was held recently as part of SemEval Cortis et al. (2017), and provided a small dataset containing company names. This dataset contains only 1,000 news _headlines_, of which only 165 instances mention more than one company name, of which only 20 instances contain names with different polarities (positive for one company but negative for another). Thus, using entity-oriented methods on this dataset may not lead to an advantage in performance. Of the ten best-performing systems on the news sentiment task, many used sentence-level classification with no treatment of target company Rotim et al. (2017); Cabanski et al. (2017); Ghosal et al. (2017); Kumar et al. (2017); others replace the target name with a special token Mansar et al. (2017); Moore and Rayson (2017); Jiang et al. (2017) or use company name as a feature Kar et al. (2017), though none of the papers provide any evidence that special treatment of the target yields a gain in performance. In our experiments with the SemEval dataset Pivovarova et al. (2017) a model with explicitly specified target worked slightly worse than a baseline.

The dataset that we release with this paper is 20 times larger and contains _entire_ documents, where a given entity may be mentioned multiple times, with many different names mentioned in the same document. This corpus is suitable for experiments with entity-oriented polarity, and our experiments explicitly contrast models that take focus as an input against models that do not use the information about the target company's position.

**Transfer learning:** a.k.a. _inductive transfer_, is a technique for applying knowledge accumulated from solving one problem to improve the solution for a different problem. We use _feature transfer_, where the goal is to learn transferable representations for data, which are meaningful for multiple tasks Pan and Yang (2010); Bengio et al. (2013); Conneau et al. (2017), i.e., very general, low-level representations. On the other hand, one might consider two related tasks, and try to gain knowledge from one to help with the other. In such cases, one wishes to transfer representations at a much _higher level_Glorot et al. (2011). An analysis of the trade-offs between generality and specificity of learned features can be found at Yosinski et al. (2014). Deep learning with knowledge transfer has been previously applied to sentiment analysis in the context of domain adaptation Glorot et al. (2011) and cross-lingual applications Zhou et al. (2016). In our experiments, we apply knowledge transfer from event classification to sentiment analysis 

The Model

We train a classifier for entity-oriented polarity, which receives on input a text and a "_focus_" vector--the positions of mentions of the target company in text--and outputs the polarity for this company. For this purpose we extend state-of-the-art models in Kim (2014). The rationale for introducing focus is that polarity is not a feature of the text as a whole, but of each company mention; two company mentions in a text may have opposing polarities, and the model needs be able to distinguish them.

The architecture of the model is shown in Figure 1. The inputs are fed into the network as sentences of a fixed size, zero-padded; each word is a fixed-dimensional embedding vector complemented with a scalar indicating the _focus_. The focus vector is shown in darker grey in Figure 1, with the the company mention framed in red. This provides an additional dimension to the word embedding, and is crucial for distinguishing between instances that differ only in focus and polarity.

The inputs are fed into a layer of convolutional filters with multiple widths, optionally followed by deeper convolutional layers. The results of the last convolutional layer are max-pooled, producing a vector with one scalar per filter, which is then fed into a fully-connected layer with dropout regularization and a soft-max output layer. The output is a 2-dimensional vector that is a probability distribution over the two possible outcomes: positive and negative. In manual annotation we use five values: "very negative" [1], "somewhat negative" [.7 .3], "neutral" [.5 .5], "somewhat positive" [.3 .7] and "very positive" [0]. The model may output any possible distribution. The loss is cross-entropy between the network's output and the true distribution; the loss updates the weights via back-propagation.

We represent words by embeddings, trained using the GloVe algorithm Pennington et al. (2014) on a corpus of 5 million news articles. Each article was pre-processed using lemmatization and the PULS NE recognition system. All NEs of the same type are mapped to the same special token; i.e., all company names have the same embedding, all person names another, etc. We continue to train the embeddings during polarity training by updating them at each iteration. This allows the model to learn properties of words significant for polarity, such as the difference between antonyms, which may not be captured well by the initial embeddings.

## 4 Data

The dataset contains 17,354 different documents with 19,689 company names. PULS clusters news into groups, each group containing documents describing the same story.2 Then we manually annotate each group with business polarity of the most salient company names.

Footnote 2: The grouping algorithm takes into account the semantic similarity of the keywords, and the distributions of NEs within the documents Escoter et al. (2017)

In our experiments, each training instance is the first five sentences in the document beginning from the first mention of the focus company. This choice was made because typically the beginning of an article carries information about the principal event, whereas later text contains background information which may mention the company, but where the polarity may be different. In case this processing results in identical instances, we remove duplicates, and keep only one copy.

The resulting dataset used in the experiments contains 14,172 distinct instances. The distribution of the data among the polarity classes is shown in Table 1. Instances labeled "contradictory" are not used for testing and training at present. The data were split into five folds for cross-validation.

We also have a separate, large collection of news articles Pivovarova et al. (2013), which is annotated for _business events_--for example, _Merger_, _Contract_, _Investment_, _Product launch_, _Product recall_, _Fraud_, _Bankruptcy_--291 labels in all. An article may have multiple event labels. Some of these labels may imply (or strongly correlate with) positive or negative polarity. We attempt to exploit this large data to improve polarity prediction. To this end, we attempt two approaches, with several variations: **manual mapping** and **high-level feature transfer**.

For **manual mapping**, we manually selected those labels which we believe most clearly imply a polarity: e.g., _Investment_, _Product launch_ and _Sponsorship_ are considered positive, while _Fraud_, _Layoff_ and _Bankruptcy_ are negative; in all, we identified 26 "positive" and 12 "negative" labels. Using

\begin{table}
\begin{tabular}{l c|c c} \hline
**Class** & **\#instances** & **Class** & **\#instances** \\ \hline very positive & 2709 & very negative & 2532 \\ positive & 4001 & negative & 4645 \\ neutral & 285 & contradictory & 146 \\ \hline \end{tabular}
\end{table}
Table 1: Class distribution in annotated data.

 

only these 38 event labels, we constructed a training set, removing documents with labels that result in no polarity, or conflicting polarities. Further, since it is impossible to know to which company the label refers, only documents whose headlines and the first sentence contain _exactly one_ company mention were kept. (For example, if one company goes bankrupt and another acquires its assets--such documents are not used.)

The resulting dataset is highly skewed with 90% of the data positive. To assure that the positive and negative subsets have similar size, we apply _random undersampling_Dendamrongvit and Kubat (2010), i.e., we use a random subset of the positive documents. Of more than two million documents in the original event corpus, 100,000 have a non-ambiguous negative label and mention exactly one company. The resulting dataset consists of 200,000 documents; 10% is used as a development set to decide when to stop training. We use this newly generated 200K document _event corpus_ in 2 ways: _Tuning:_ a two-stage learning procedure where the model is first trained using the event corpus, and then is tuned using the smaller polarity corpus.

_Training on combined data:_ in this strategy, data from both corpora are mixed together, and used for training in random order.

In **high-level feature transfer**, we aim to reuse relatively high-level, task-specific features. We initially train a model to predict event types--using _all_ event labels and _all_ documents, irrespective of how many companies they mention. This requires a change in the models: because event labels are not mutually exclusive, we use a sigmoid function instead of soft-max in the topmost layer. After the event model is fully trained on the event labels, we strip off the last fully-connected layer of the network, replace it with a two-class output layer for polarity, and resume training using the smaller polarity dataset. We expect that the more task-specific features--ones obtained closer to the output layer--will be useful to determine polarity values, due to the latent relatedness between the two tasks. Thus, we keep almost the entire model, with the exception of the very final layer.

From the large dataset labeled with events, we use 10% as a development set, to determine when to stop training, and to find the best model; another 10% is used as a test set. Results from representative runs are shown in Table 2.

The bigger model gives better performance--with a much larger number of filters (1000 vs. 128). It is not possible to use such large models for learning polarity _without_ transfer, because these models are trained on much smaller data and would quickly overfit. Therefore, in subsequent experiments we use two convolutional layers with filter sizes 3,4,5, with 128 filters of each size. 3

Footnote 3: The overall accuracy of the method can likely be improved further by careful tuning of hyper-parameters. Here we focus on the comparison of architectures, and defer hyperparameters for future work.

## 5 Experiments

We present experiments with focus and knowledge transfer variants. Table 3 shows the results for each model variant, averaged across five-fold cross-validation. We report accuracy and cosine simila

Figure 1: A model architecture with focus vector and two convolution layers



ity between the model output and the annotation.

To compute accuracy as follows. In _annotation_ we treat polarity detection as a _three-way_ classification task; values inside \([-0.1,0.1]\) are considered neutral; values further from \(0.0\) are positive or negative. However, for reasons presented below, the models do not do well on identifying neutral instances. Thus, in the experiments presented here, we evaluate prediction of _binary_ polarity4: negative vs. positive or neutral. Accuracy measures how often a model _blunders_, and predicts negative polarity rather than positive or neutral, or vice versa.

Footnote 4: Due to the industry-level requirements: in business news negative polarity has important implications; “neutral” coverage may even be viewed mildly positive, as the entity mentioned is receiving (non-negative) publicity, etc.

Cosine similarity5 is computed by collecting all of the model's polarity probabilities into one vector and one for the manually assigned polarities, and measuring the cosine between the vectors; also, polarities are mapped into the interval \([-1,1]\). This gives a measure of closeness between model prediction and the ground truth, including differences between "positive" and "very positive" classes.

Footnote 5: This is the official measure of the SemEval business polarity classification task Cortis et al. (2017); we include it because it may be useful for indirect comparison of results.

As the results show, accuracy and cosine similarity do not produce consistent rankings, because they measure different aspects of performance. From a practical, user-oriented point of view, it may be more important that a model avoid gross errors, rather than capturing subtle shades of polarity. In manual annotation we noticed that some distinctions ("positive" vs. "very positive") is far from clear for human annotators. Thus, we are interested in the models that yield the best _accuracy_.

In addition, we used a SVM classifier as a baseline. The baseline does not use any information about the target company. We use a one-vs-all strategy to obtain three-way classification. For the baseline we report only the accuracy, since this method does not directly produce probabilities.

## 6 Discussion

Knowledge transfer:Table 3 shows that high-level feature transfer outperforms manual mapping. The main reason might be that feature transfer can benefit from a very large corpus of 2 million documents, while only 200,000 documents can be used with the manual mapping approach, which prevents us from training larger models due to over-fitting. The mapped dataset may suffer from other problems, resulting from how it is created. First, it contains no articles with _neutral_ polarity--if an article has no positive or negative label, we cannot assume it to be neutral. For example, articles labeled _Corporate appointments_ may have positive or negative polarity. Second, although we choose only the most "trusted" event labels for mapping to polarity, the dataset still contains noise: e.g., a document labeled as _Merger_ and assumed to be positive may in fact discuss a canceled merger. Third, since we use only a small subset of the labels, the dataset is highly skewed and incomplete--most event types and data are not used. Most importantly, using manually mapped data, a model is trained to perform a task different from our target--it learns to dis

\begin{table}
\begin{tabular}{l|c|c|c} \hline
**Hyper-Parameters** & **rec** & **prec** & **F1** \\ \hline conv 2; filters 128; sizes {3,4,5} & 35.85 & 75.42 & 48.53 \\ conv 1; filters 1000; sizes {3,7,11} & 50.80 & 58.13 & **53.89** \\ \hline \end{tabular}
\end{table}
Table 2: Event classification results

\begin{table}
\begin{tabular}{l|c|c|c} \hline
**Transfer Strategy** & **Focus** & **Accuracy** & **Cosine** \\ \hline none & – & 81.22 & 71.51 \\ none & + & 81.44 & 72.93 \\ manual tuning & + & 82.07 & **73.98** \\ manual combined data & + & 82.01 & 70.92 \\ \hline feature transfer & – & 83.94 & 71.17 \\ feature transfer & + & **84.44** & 71.76 \\ \hline _baseline_ (SVM) & & 52.02 & \\ \hline \end{tabular}
\end{table}
Table 3: Experimental results (multiplied by 100, for readability.) 

tinguish not positive vs. negative polarity, but one (sub-)set of event labels from another. We cannot assume that the model learns polarity patterns, only that polarity _correlates_ with certain event types.

**Focus:** The results indicate that focus further improves performance.6 On some test instances, models without focus outperforms models with focus--this happens when polarity expressions lie outside the filter window around the focus company, e.g., as in Example 1 in Table 4.

Footnote 6: Results are statistically significant at \(p<0.05\) or lower.

If two companies within the same text have opposite polarities, a model without focus can assign the correct polarity to at most one of them, as in example 2 in Table 4. Such cases are rare in our dataset; typically, when two companies are involved in the same event, they have the same polarity, e.g., when they strike a deal. Only 6% of instances in our dataset have a paired instance that has identical text but different focus and opposite polarity. Another case when focus is useful is when a document contains much background information, which may contain opposite polarity statements. Estimating the number of such cases is an arduous task. Since in some cases a model with focus performs worse than a model without focus, there is no clear gain in that regard. However, the best-performing transfer strategy works slightly better, as seen from Table 3.

**Neutral polarity:** Another observation is that all of our models have difficulty in detecting neutral polarities, as shown in Example 3 (which is about Facebook's CEO, rather than the company itself). Neutral examples are rare in our dataset, as shown in Table 1. This is probably the main reason why the models are unable to distinguish neutral polarity. This problem may be helped by annotating more neutral instances.

## 7 Conclusion

We address the problem of entity-oriented business polarity detection. The main contributions are: I. a dataset of 17,000 annotated documents, which is an order of magnitude larger than any previously available resources for this task;7 II. we propose benchmark solutions to this problem, based on CNN architectures originally intended for document-level polarity classification, modified for _entity-oriented_ polarity classification by explicitly incorporating _focus_ into the model; III. we demonstrate that performance can be improved via _transfer learning_, by training a network on a much larger corpus, which is annotated for a different, distantly related task--namely, classification of event types.

Footnote 7: The corpus is available at puls.cs.helisinki.fi/polarity

We compare manual label mapping with transferring high-level features, and demonstrate that the latter approach performs better, and is less subjective; i.e., features relevant for finding event types work better than a simplistic mapping between the two tasks. The rationale behind this is that business polarity is latently inherent in the event types themselves: some event types carry a positive or negative polarity, while others do not indicate an unambiguous polarity. Therefore, attempting to map event labels directly to polarity is problematic.

For manual mapping of event labels, we can use only documents with exactly one company and "unambiguous" event labels, while for transfer learning we can use the entire event dataset, which lets us use much more data for training bigger models.

High-level feature transfer yields \(15.8\)% error reduction--\(81\)% to 84% accuracy--as compared to using only the small polarity-annotated corpus.

\begin{table}
\begin{tabular}{|c|l|c|c|c|c|} \hline  & **Example** & **Truth** & +**Focus** & **–Focus** & **Comment** \\ \hline
1 & **Valeant to sell Dendeon unit to Sampower for §820 & -1.0 & 0.022 & -0.322 & The model without focus per-forms better since the company name is mentioned far away from the polarity expression and there is another name in between. \\  & million. Canada’s Valeant Pharmaceuticals International Inc. said its affiliate will sell Its Dendron cancer business to Sampower Group Co. Ltd. for $819.9 million, as the drugmaker continues to shed its non-core assets _to repay debt_. & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline
3 & **Facebook** CEO Mark Zuckerberg and his wife are dropping controversial suits they filed in December to buy small plots of land that are part of a [...] & 0.0 & -0.743 & -0.397 & Text is about Facebook CEO, not the company itself. None of the models handle neutral com-party mentions well. \\ \hline \end{tabular}
\end{table}
Table 4: Model comparison: CNN with/without focus using transfer. Company in focus is in bold 



## Acknowledgements

This work was supported in part by Tekes (the Finnish Funding Agency for Technology and Innovation) Project _iMEDL: Digital Media Evolution through Deep Learning_, Grant number 5933/31/2017.

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