Venue: NAACL 2019

# Combining Sentiment Lexica with a

Multi-View Variational Autoencoder

Alexander Hoyle\({}^{\text{\text{\textdegree}}}\)Lawrence Wolf-Sonkin\({}^{\text{\textdagger}}\)Hanna Wallach\({}^{\text{\textdegree}}\)Ryan Cotterell\({}^{\text{\textdagger}}\)Isabelle Augenstein\({}^{\text{\textdagger}}\)

###### Abstract

When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.

1Department of Computer Science, Johns Hopkins University, Baltimore, USA

2Microsoft Research, New York City, USA

3Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

4Department of Computer Science, University of Copenhagen, Copenhagen, Denmark alexander.hoyle.17@ucl.ac.uk, lawrencews@jhu.edu hanna@dirichlet.net, rdc42@cam.ac.uk, augenstein@di.ku.dk

## 1 Introduction

Sentiment lexica provide an easy way to automatically label texts with polarity values, and are also frequently transformed into features for supervised models, including neural networks Palogiannidi et al. (2016); Ma et al. (2018). Indeed, given their utility, a veritable cottage industry has emerged focusing on the design of sentiment lexica. In practice, using any single lexicon, unless specifically and carefully designed for the particular domain of interest, has several downsides. For example, any lexicon will typically have low coverage compared to the language's entire vocabulary, and may have misspecified labels for the domain. In many cases, it may therefore be desirable to combine multiple sentiment lexica into a single representation. Indeed, some research on unifying such lexica has emerged Emerson and Declerck (2014); Altrabsheh et al. (2017), borrowing ideas from crowdsourcing Raykar et al. (2010); Hovy et al. (2013). However, this is a non-trivial task, because lexica can use binary, categorical, or continuous scales to quantify polarity--in addition to different interpretations for each--and thus cannot easily be combined. In Fig. 1, we show an example of the same word labeled using different lexica to illustrate the nature of the challenge.

To combine sentiment lexica with disparate scales, we introduce SentiVAE, a novel multi-view variant of the variational autoencoder VAE Kingma and Welling (2014). SentiVAE, visualized as a graphical model in Fig. 2, differs from the original VAE in two ways: (i) it uses a Dirichlet latent variable (rather than a Gaussian) for each word in the combined vocabulary, and (ii) it has multiple emission distributions--one for each lexicon. Because the latent variables are shared across the lex

Figure 1: A depiction of the “encoder” portion of SentiVAE. The word _peppy_ has polarity values of 0.65 and _pos_ in the SenticNet and Hu-Liu lexica, respectively. These values are “encoded” into two three-dimensional vectors, which are then summed and added to \((1,1,1)\) (not shown) to form the parameters of a Dirichlet over the latent representation of the word’s polarity value.



ica, we are able to derive a common latent representation of the words' polarities. The resulting model is spiritually related to a multi-view learning approach [20], where each view corresponds to a different lexicon. Experimentally, we use SentiVAE to combine six commonly used English-language sentiment lexica with disparate scales.

We evaluate the resulting representation via a text classification task involving nine English-language sentiment analysis datasets. For each dataset, we transform each text into an average polarity value using either our representation, one of the six commonly used sentiment lexica, or a straightforward combination thereof. We then train a classifier to predict the overall sentiment of each text from its average polarity value. We find that our representation outperforms the individual lexica, as well as the straightforward combination for some datasets. Our representation is particularly efficacious for datasets from domains that are not well-supported by standard sentiment lexica.1

Footnote 1: Our representation and code are available at [https://github.com/ahoho/SentiVAE](https://github.com/ahoho/SentiVAE).

The existing research that is most closely related to our work is SentiMerge [35], a Bayesian approach for aligning sentiment lexica with different continuous scales. SentiMerge consists of two steps: (i) aligning the lexica via rescaling, and (ii) combining the rescaled lexica using a Gaussian distribution. The authors perform token-level evaluation using a single sentiment analysis dataset where each token is labeled with its contextually dependent sentiment. Because SentiMerge can only combine lexica with continuous scales, we do not include it in our evaluation.

## 2 Sentiment Lexica and Scales

We use the following commonly used English-language sentiment lexica: SentiWordNet [4], MPQA [9], SenticNet 5 [7], Hu-Liu [8], GI [6], and VADER [10]. Descriptive statistics for each lexicon are shown in Tab. 1. Each word in SentiWordNet is labeled with two real values, each in the interval \([0,1]\), corresponding to the strength of positive and negative sentiment (e.g., the label \((0~{}0)\) is neutral, while the label \((1~{}0)\) is maximally positive). Each word in VADER is labeled by ten different human evaluators, with each evaluator providing a polarity value on a nine-point scale (where the midpoint is neutral), yielding a 10-dimensional label. MPQA, Hu-Liu, and GI all use binary scales. Lastly, each word in SenticNet is labeled with a real value in the interval \([-1,1]\), where 0 is neutral.

## 3 SentiVAE

We first describe a figurative generative process for a single sentiment lexicon \(d\in\mathcal{D}\), where \(\mathcal{D}\) is a set of sentiment lexicia. Imagine there is a true (latent) polarity value \(\mathbf{z}^{w}\) associated with each word \(w\) in the lexicon's vocabulary. When the lexicon's creator labels that word according to their chosen scale (e.g., thumbs-up or thumbs-down, a real value in the interval \([0,1]\)), they deterministically transform this true value to their chosen scale via a function \(f(\,\cdot~{};\mathbf{\theta}_{d})\).2 Sometimes, noise is introduced during this labeling process, corrupting the label as it leaves the ethereal realm and producing the (observed) polarity label \(\mathbf{x}_{d}^{w}\). They then add this potentially noisy label to the lexicon.

Footnote 2: Parameterized by lexicon-specific weights \(\mathbf{\theta}_{d}\).

Given a lexicon of observed polarity labels, the latent polarity values can be inferred using a VAE. The original VAE posits a generative model of observed data \(\mathcal{X}\) and latent variables \(\mathcal{Z}\): \(P(\mathcal{X},\mathcal{Z})=P(\mathcal{X}\mid\mathcal{Z})\,P(\mathcal{Z})\). Inference of \(\mathcal{Z}\) then proceeds by approximating the (intractable) posterior \(P(\mathcal{Z}\mid\mathcal{X})\) with a Gaussian distribution, factorized over the individual latent variables. A parameterized encoder function compresses \(\mathcal{X}\) into \(\mathcal{Z}\), while a parameterized decoder function reconstructs \(\mathcal{X}\) from \(\mathcal{Z}\).

SentiVAE extends the original VAE model to combine multiple lexica with disparate scales, producing a common latent representation of the polarity value for each word in the combined vocabulary.

Generative process.Given a set of sentiment lexica \(\mathcal{D}\) with a combined vocabulary \(\mathcal{W}\), SentiVAE posits a common latent representation \(\mathbf{z}^{w}\) of the polarity value for each word \(w\in\mathcal{W}\), where \(\mathbf{z}^{w}\) is a three-dimensional categorical distribution over

\begin{table}
\begin{tabular}{l l c c} \hline \hline Lexicon & Source & \(N\) & Dom \\ \hline SentiWordNet & WordNet & 14107 & \([-1,1]^{2}\) \\ MPQA & Newswire & 4397 & \(\{0,1\}\) \\ SenticNet & — & 100000 & \([-1,1]\) \\ Hu–Liu & Product reviews & 6790 & \(\{0,1\}\) \\ GI & — & 4206 & \(\{0,1\}\) \\ VABER & Social media & 7489 & \(\{0,\dots,8\}\)\({}^{10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for the sentiment lexica. \(N\): vocabulary size. Dom: Domain of polarity values.

 

the sentiments _positive_, _negative_, and _neutral_.

The generative process starts by drawing each latent polarity value \({\mathbf{z}}^{w}\) from a three-dimensional Dirichlet prior, parameterized by \({\mathbf{\alpha}}^{w}=(1,1,1)\):

\[{\mathbf{z}}^{w}\sim\text{Dir}({\mathbf{\alpha}}^{w}). \tag{1}\]

If the word is uncontroversial,3 we spur this prior somewhat using the number of lexica in which the word appears \(c(w)\). Specifically, we add \(c(w)\) to the parameter for the sentiment associated with that word in the lexica, e.g., \({\mathbf{\alpha}}^{\textsc{superb}}=(1+c(\textsc{superb}),1,1)\). This has the effect of regularizing the inferred latent polarity value toward the desired distribution over sentiments.

Footnote 3: We say that a word is uncontroversial if there is strong agreement across the sentiment lexica in which it appears. Even without this spurring, the inferred latent representation typically separates into the three sentiment classes, but performance on our text classification task is somewhat diminished.

Having generated \({\mathbf{z}}^{w}\), the process proceeds by "decoding" \({\mathbf{z}}^{w}\) into each lexicon's chosen scale. First, for each lexicon \(d\in\mathcal{D}\), \({\mathbf{z}}^{w}\) is deterministically transformed via neural network \(f(\,\cdot\,,\,{\mathbf{\theta}}_{d})\) with a single 32-dimensional hidden layer, parameterized by lexicon-specific weights \({\mathbf{\theta}}_{d}\):

\[{\mathbf{\rho}}_{d}^{w}=f({\mathbf{z}}^{w};{\mathbf{\theta}}_{d}). \tag{2}\]

The transformed value \({\mathbf{\rho}}_{d}^{w}\) is then used to generate the (observed) polarity label \({\mathbf{x}}_{d}^{w}\) for that lexicon:

\[{\mathbf{x}}_{d}^{w}\sim P_{d}({\mathbf{x}}_{d}^{w}\mid{\mathbf{\rho}}_{d}^{ w}). \tag{3}\]

The dimensionality of \({\mathbf{\rho}}_{d}^{w}\) and the emission distribution \(P_{d}\) are lexicon-specific. For SentiWordNet, \(P_{d}\) is a two-dimensional Gaussian with mean \({\mathbf{\rho}}_{d}^{w}\) and a diagonal covariance matrix equal to \(0.01{\mathbf{I}}\); for VADER, \(P_{d}\) consists of ten nine-dimensional categorical distributions, collectively parameterized by \({\mathbf{\rho}}_{d}^{w}\); for MPQA, Hu-Liu, and GI, \(P_{d}\) is a Bernoulli distribution, parameterized by \({\mathbf{\rho}}_{d}^{w}\); and for SenticNet, \(P_{d}\) is a univariate Gaussian with mean and variance each an element in a two-dimensional \({\mathbf{\rho}}_{d}^{w}\).

Inference.Inference involves forming the posterior distribution over the latent polarity values \(\mathcal{Z}\) given the observed polarity labels \(\mathcal{X}\). Because computing the normalizing constant \(P(\mathcal{X})\) is intractable, we instead approximate the posterior with a family of distributions \(Q_{\mathbf{\lambda}}(\mathcal{Z})\), indexed by variational parameters \({\mathbf{\lambda}}\). Specifically, we use

\[Q_{\mathbf{\lambda}}(\mathcal{Z})=\prod_{w\in\mathcal{W}}Q_{{\mathbf{\beta}}^{w}}({ \mathbf{z}}^{w})=\prod_{w\in\mathcal{W}}\text{Dir}({\mathbf{\beta}}^{w}). \tag{4}\]

To construct \({\mathbf{\beta}}^{w}\), we first define a neural network \(g(\cdot;\,{\mathbf{\phi}}_{d})\), with a single 32-dimensional hidden layer, which "encodes" \({\mathbf{x}}_{d}^{w}\) into a three-dimensional vector. The output of this neural network is then transformed via a softmax as follows:

\[{\mathbf{\omega}}_{d}^{w} =\text{softmax}\big{(}g({\mathbf{x}}_{d}^{w};\,{\phi}_{d})\big{)} \tag{5}\] \[{\mathbf{\beta}}^{w} =1+\sum_{d\in\mathcal{D}}{\mathbf{\omega}}_{d}^{w}. \tag{6}\]

The intuition behind \({\mathbf{\beta}}_{w}\) can be understood by appealing to the "pseudocount" interpretation of Dirichlet parameters. Each lexicon contributes exactly one pseudocount, divided among _positive_, _negative_, and _neutral_, to what would otherwise be a symmetric, uniform Dirichlet distribution. As a consequence of this construction, words that appear in more lexica will have more concentrated Dirichlets. Intuitively, this property is appealing.

We optimize the resulting ELBO objective Blei et al. (2017) with respect to the variational parameters via stochastic variational inference Hoffman

\begin{table}
\begin{tabular}{l l l l} \hline \hline Dataset & Source & \(N\) & Classes \\ \hline IMDB & Movies & 25000 & 2 \\ Ye1p & Product reviews & 100000 & 5 / 3 \\ SemEval & Twitter & 7668 & 3 \\ MultiDom & Product reviews & 6500 & 2 \\ ACL & Scientific reviews & 248 & 5 / 3 \\ ICLR & Scientific reviews & 2166 & 10 / 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics for the training portions of the sentiment analysis datasets. \(N\): number of texts.

Figure 2: Generative model for SentiVAE.

 

et al., 2013) using Adam (Kingma and Ba, 2015) in the Pyro framework (Bingham et al., 2018). The standard reparameterization trick used in the original VAE does not apply to models with Dirichlet-distributed latent variables, so we use the generalized reparameterization trick of Ruiz et al. (2016).

## 4 Experiments and Results

To evaluate our approach, we first use SentiVAE to combine the six lexica described in SS2. For each word \(w\) in the combined vocabulary, we obtain an estimate of \({\mathbf{z}}^{w}\) by taking the mean of \(Q_{{\mathbf{\beta}}^{w}}({\mathbf{z}}^{w})=\text{Dir}({\mathbf{\beta}}^{w})\)--i.e., by normalizing \({\mathbf{\beta}}^{w}\). We compare this representation to using \({\mathbf{\beta}}^{w}\) directly, because \({\mathbf{\beta}}^{w}\) contains information about SentiVAE's certainty about the word's latent polarity value. We evaluate our common latent representation via a text classification task involving nine English-language sentiment analysis datasets: IMDB Maas et al. (2011), Yelp Zhang et al. (2015), SemEval 2017 Task 4 (SemEval, Rosenthal et al. (2017)), multi-domain sentiment analysis (MultiDom, Blitzer et al. (2007)), and PeerRead Kang et al. (2018) with splits ACL 2017 and ICLR 2017 (Kang et al., 2018). Each dataset consists of multiple texts (e.g., tweets, articles), each labeled with an overall sentiment (e.g., _positive_). Descriptive statistics for each dataset are shown in Tab. 2. For the datasets with more than three sentiment labels, we consider two versions--the original and a version with only three (bucketed) sentiment labels.

For each dataset, we transform each text into an average polarity value using either our representation, one of the six lexica,4 or a straightforward combination thereof, where the polarity value for each word in the (combined) vocabulary is a 16-dimensional vector that consists of a concatenation of polarity values. (Unlike SentiVAE, this concatenation does not yield a single sentiment lexicon that retains scale coherence, while achieving maximal coverage over words.) Specifically, we replace each token with its corresponding polarity value, and then average the these values (Go et al., 2009; Ozdemir and Bergler, 2015; Kiritchenko et al., 2014). We then use the training portion of the dataset to learn a logistic regression classifier to predict the overall sentiment of each text from its average polarity value. Finally, we use the testing portion to compute the accuracy of the classifier.

Footnote 4: We bucket the upper four and lower four points of VADER’s nine-point scale, to yield a three-point scale. Without this bucketing, our representation outperforms VADER on four of the nine datasets. We do not bucket VADER when using it in SentiVAE or in the straightforward combination.

Results.The results in Tab. 3 show that our representation using \({\mathbf{\beta}}^{w}\) outperforms the individual lexica for all but one dataset, and that our representation using the mean of \(Q_{{\mathbf{\beta}}^{w}}({\mathbf{z}}^{w})\) outperforms them for six datasets. This is likely because SentiVAE has a richer representation of sentiment than any individual lexicon, and it has greater coverage over words (see Tab. 4). The results in Tab. 5 support the former reason: even when we limit the words in our representation to match those in an individual lexicon, our representation still outperforms the individual lexicon. Unsurprisingly, our representation especially outperforms lexica with unidimensional scales. We also find that our representation outperforms the straightforward combination for datasets from domains that are not well supported by the individual lexica (see Tabs. 1 and 2 for lexicon and dataset sources, respectively). By combining lexica from different domains, our representation captures a general notion of sentiment that is not tailored to any specific domain.

## 5 Conclusion

We introduced a generative model of sentiment lexica to combine disparate scales into a common

\begin{table}
\begin{tabular}{r r c c c c c c c c} \hline  & **IMDB 2C** & **Yelp 5C** & **Velp 3C** & **SemEval 3C** & **MultiDm 2C** & **ACL 5C** & **ACL 3C** & **ICLR 10C** & **ICLR 3C** \\ \hline
**SentiVAE** & E\({}_{\mathrm{O}}[x^{w}]\) & 72.7 & 49.8 & 57.5 & 46.0 & 70.8 & 66.7 & 73.3 & 92.6 & **87.0** \\
**SentiVAE** & \({\mathbf{\beta}}^{w}\) & 73.4 & 49.7 & 59.4 & **52.2** & 74.7 & **73.3** & **80.0** & 92.6 & 86.5 \\ \hline
**SentiWordNet** & 63.4 & 36.0 & 47.6 & 32.2 & 62.0 & 60.0 & 53.3 & 89.1 & 83.5 \\
**MPQA** & 65.4 & 44.0 & 53.0 & 29.9 & 67.4 & 60.0 & 53.3 & 89.1 & 83.5 \\
**SentiSetNet** & 60.5 & 38.4 & 43.4 & 37.2 & 62.3 & 60.0 & 53.3 & 89.1 & 83.9 \\
**Hu-Liu** & 67.2 & 46.6 & 56.4 & 31.5 & 69.4 & 60.0 & 53.3 & 89.1 & 83.5 \\
**GI** & 58.4 & 40.7 & 47.9 & 31.3 & 61.6 & 60.0 & 53.3 & 89.1 & 83.5 \\
**VADER** & 71.7 & 46.8 & 59.3 & 38.5 & 73.5 & 66.7 & 66.7 & **94.3** & 86.1 \\ \hline
**Combined** & **75.6** & **51.0** & **64.1** & 50.6 & **75.4** & 66.7 & 66.7 & 93.9 & 86.1 \\ \hline \end{tabular}
\end{table}
Table 3: Classification accuracies for our representation, six lexica, and a straightforward combination thereof.

 

latent representation, and realized this model with a novel multi-view variational autoencoder, called SentiVAE. We then used SentiVAE to combine six commonly used English-language sentiment lexica with binary, categorical, and continuous scales. Via a downstream text classification task involving nine English-language sentiment analysis datasets, we found that our representation outperforms the individual lexica, as well as a straightforward combination thereof. We also found that our representation is particularly efficacious for datasets from domains that are not well-supported by standard sentiment lexica. Finally, we note that our approach is more general than SentiMerge (Emerson and Declerck, 2014). While SentiMerge can only combine sentiment lexic with continuous scales, SentiVAE is designed to combine lexica with disparate scales.

## Acknowledgements

We would like to thank to Adam Forbes for the design of Fig. 1. We further acknowledge the support of the NVIDIA Corporation with the donation of the Titan Xp GPU used to conduct this research.

## References

* Altrabsheh et al. (2017) Nabeela Altrabsheh, Mazen El-Masri, and Hanady Mansour. 2017. Combining Sentiment Lexicons of Arabic Terms. In _AMCIS_. Association for Information Systems.
* Baccianella et al. (2010) Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. 10(2010):2200-2204.
* Bingham et al. (2018) Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletos, Rohit Singh, Paul A. Szerlip, Paul Horsfall, and Noah D. Goodman. 2018. Pyro: Deep Universal Probabilistic Programming. _CoRR_, abs/1810.09538.
* Blei et al. (2017) David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. 2017. Variational inference: A review for statisticians. _Journal of the American Statistical Association_, 112:859-877.
* Blitzer et al. (2007) John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In _Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics_, pages 440-447, Prague, Czech Republic. Association for Computational Linguistics.
* Cambria et al. (2014) Erik Cambria, Daniel Olsher, and Dheeraj Rajagopal. 2014. SenticNet 3: A Common and Commonsense Knowledge Base for Cognition-driven Sentiment Analysis. In _Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence_, AAAI'14, pages 1515-1521. AAAI Press.
* Emerson and Declerck (2014) Guy Emerson and Thierry Declerck. 2014. SentiMerge: Combining Sentiment Lexicons in a Bayesian Framework. In _Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing_, pages 30-38. Association for Computational Linguistics and Dublin City University.
* Go et al. (2009) Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter Sentiment Classification using Distant Supervision. _Processing_, pages 1-6.
* Hoffman et al. (2013) Matthew D. Hoffman, David M. Blei, Chong Wang, and John William Paisley. 2013. Stochastic variational inference. _Journal of Machine Learning Research_, 14(1):1303-1347.
* Hovy et al. (2013) Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, and Eduard H. Hovy. 2013. Learning Whom to Trust with MACE. In _Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 1120-1130. Association for Computational Linguistics.
* Hu and Liu (2004) Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In _Proceedings of the Tenth

\begin{table}
\begin{tabular}{r c c c c} \hline \hline  & IMDB & SemEval & Multi & ICLR \\ \hline
**SentiVAE** & 70 & 64 & 81 & 71 \\ \hline
**SentiWordNet** & 15 & 14 & 24 & 16 \\
**MPQA** & 10 & 7 & 18 & 9 \\
**SentieNet** & 40 & 39 & 53 & 45 \\
**Hu-Liu** & 7 & 5 & 13 & 5 \\
**G1** & 8 & 7 & 15 & 6 \\
**VADER** & 7 & 6 & 13 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coverage over words (percentage) by lexicon for the training portions of four of the nine datasets.

\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**IMDB 2C**} & \multicolumn{2}{c}{**SemEval 3C**} \\ \hline  & SV & Lex & SV & Lex \\ \hline
**SentiVAE** & **74.7** & – & **72.4** & – \\ \hline
**SentiWordNet** & 70.6 & 63.4 & 67.4 & 55.1 \\
**MPQA** & 73.5 & 66.6 & 62.6 & 51.8 \\
**SentieNet** & 74.4 & 60.9 & 72.1 & 59.5 \\
**Hu-Liu** & 73.6 & 68.4 & 59.1 & 51.1 \\
**GI** & 71.4 & 59.3 & 63.8 & 54.0 \\
**VADER** & 73.6 & 73.1 & 60.9 & 58.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracies for a 10% validation portion of two of the datasets. The first row, labeled SentiVAE, contains the classification accuracy for our representation using \(\beta^{w}\). Subsequent (lexicon-specific) rows compare our representation (SV), restricted to the vocabulary of that lexicon, to the lexicon itself (Lex).

 

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining_, pages 168-177. ACM.
* Hutto and Gilbert (2014) C. J. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In _Eighth International Conference on Weblogs and Social Media (ICWSM-14)_.
* Kang et al. (2018) Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard H. Hovy, and Roy Schwartz. 2018. A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications. In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_, pages 1647-1661. Association for Computational Linguistics.
* Kingma and Ba (2015) Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In _International Conference on Learning Representations (ICLR)_.
* Kingma and Welling (2014) Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In _Proceedings of the Second International Conference on Learning Representations (ICLR)_.
* Kiritchenko et al. (2014) Svetlana Kiritchenko, Xiaodan Zhu, and Saif M. Mohammad. 2014. Sentiment Analysis of Short Informal Texts. _Journal of Machine Learning Research_, 50:723-762.
* Ma et al. (2018) Yukun Ma, Haiyun Peng, and Erik Cambria. 2018. Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM. In _AAAI_, pages 5876-5883. AAAI Press.
* Maas et al. (2011) Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning Word Vectors for Sentiment Analysis. In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_, pages 142-150, Portland, Oregon, USA. Association for Computational Linguistics.
* Ozdemir and Bergler (2015) Canberk Ozdemir and Sabine Bergler. 2015. A Comparative Study of Different Sentiment Lexica for Sentiment Analysis of Tweets. In _Proceedings of the International Conference Recent Advances in Natural Language Processing_, pages 488-496. INCOMA Ltd. Shoumen, BULGARIA.
* Palogiannidi et al. (2016) Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Nikolaos Malandrakis, Haris Papageorgiou, Shriakanth Narayanan, and Alexandros Potamianos. 2016. Tueseter at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation. In _Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)_, pages 155-163. The Association for Computer Linguistics.
* Raykar et al. (2010) Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. 2010. Learning From Crowds. _Journal of Machine Learning Research_, 11:1297-1322.
* Rosenthal et al. (2017) Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017. SemEval-2017 Task 4: Sentiment Analysis in Twitter. In _Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)_, pages 502-518, Vancouver, Canada. Association for Computational Linguistics.
* Ruiz et al. (2016) Francisco R. Ruiz, Michalis K. Titsias, and David M. Blei. 2016. The Generalized Reparameterization Gradient. In _Advances in Neural Information Processing Systems_, pages 460-468.
* Stone et al. (1962) Philip J. Stone, Robert F. Bales, J. Zvi Namenwirth, and Daniel M. Ogilvie. 1962. The general nquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. _Behavioral Science_, 7(4):484-498.
* Sun (2013) Shiliang Sun. 2013. A Survey on Multi-view Learning. _Neural Computing and Applications_, 23(7-8):2031-2038.
* Wilson et al. (2005) Theresa Wilson, Jansce Wiebe, and Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In _Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing_, pages 347-354, Vancouver, British Columbia, Canada. Association for Computational Linguistics.
* Zhang et al. (2015) Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level Convolutional Networks for Text Classification. In _Advances in Neural Information Processing Systems_, pages 649-657.