You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


---
abstract: 'In this paper, we introduce *UniSent* a universal sentiment lexica for 1000 languages created using an English sentiment lexicon and a massively parallel corpus in the Bible domain. To the best of our knowledge, *UniSent* is the largest sentiment resource to date in terms of number of covered languages, including many low resource languages. To create *UniSent*, we propose *Adapted Sentiment Pivot*, a novel method that combines annotation projection, vocabulary expansion, and unsupervised domain adaptation. We evaluate the quality of *UniSent* for Macedonian, Czech, German, Spanish, and French and show that its quality is comparable to manually or semi-manually created sentiment resources. With the publication of this paper, we release *UniSent* lexica as well as Adapted Sentiment Pivot related codes. method.'
author:
- Ehsaneddin Asgari
- Fabienne Braune
- Christoph Ringlstetter
- 'Mohammad R.K. Mofrad'
date:
- 
- 
title: 'UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages'
---

Introduction
============

Sentiment classification is an important task which requires either word level or document level sentiment annotations. Such resources are available for at most 136 languages [@chen-skiena-2014-building], preventing accurate sentiment classification in a low resource setup. Recent research efforts on cross-lingual transfer learning enable to train models in high resource languages and transfer this information into other, low resource languages using minimal bilingual supervision [@hangya2018; @barnes2018; @barnes2018-2]. Besides that, little effort has been spent on the creation of sentiment lexica for low resource languages (e.g.,  [@chen-skiena-2014-building; @afli2017sentiment; @darwich2017minimally]). We create and release *Unisent*, the first massively cross-lingual sentiment lexicon in more than 1000 languages. An extensive evaluation across several languages shows that the quality of *Unisent* is close to manually created resources. Our method is inspired by [@asgari2017pastPresentFuture] with a novel combination of vocabulary expansion and domain adaptation using embedding spaces. Similar to our work, [@Agic2016] also use massively parallel corpora to project POS tags and dependency relations across languages. However, their approach is based on assignment of the most probable label according to the alignment model from the source to the target language and does not include any vocabulary expansion or domain adaptation and do not use the embedding graphs.

Method
======

Our method, *Adapted Sentiment Pivot* requires a sentiment lexicon in one language (e.g. English) as well as a massively parallel corpus. Following steps are performed on this input.

####  (i) Projection of sentiment polarities: {#projection}

We project sentiment polarities from English (source language) to all languages in the parallel corpus (target languages) using the method in [@asgari2017pastPresentFuture]. This first step generates, in each target language, pairs $(wa_i, p_i)$ of words in the target vocabulary $VA$ and their associated sentiment polarities $p_i$. Vocabulary $VA$ is limited to the words at the intersection of the parallel corpus and the sentiment lexicon. Because most existing super-parallel corpora are from the Bible [@Mayer2014], $VA$ has the major drawbacks of being (i) small and (ii) in a very specific domain. To overcome the first limitation, using the embedding space we propagate the polarities $p_i$ to words outside of $VA$.

####  (ii) Vocabulary expansion of the sentiment polarities using embeddings: {#propagation}

The goal of this step is to create word-polarity pairs $(w_i, p_i)$ for a vocabulary $V$ of arbitrary size and domain. To achieve it, we train a linear model (logistic regression) using the pairs $(wa_i, p_i)$ obtained in the projection step using a general domain embedding space enriched with subword information [@bojanowski2017]. We then predict the polarities of words $wa_i$ outside of $VA$. Our evaluation in section \[evaluation\] shows that this simple method is very effective and creates accurate resources. However, this step does not handle domain shift very well. To illustrate this limitation, consider the Spanish word *sensual* (Figure \[fig:domainShift\]). In Biblical texts, this word has the connotation of *sin* which has a negative polarity. But in the Twitter domain, the same word is associated with *sexy*, which has a positive polarity. To tackle this problem, we perform an unsupervised drift weighting in the next step. Use of embeddings has two main benefits for lexicon creation: (i) Given the sentiment regularities exist in the language model-based embedding, using a rich embedding space we can expand the lexicon vocabulary. (ii) Training the classifier in the embedding space already results in domain adaptation [@rothe-etal-2016-ultradense].

#### (iii) Unsupervised drift weighting: {#adaptation}

Our weighting method is based on the divergence between the embedding spaces of vocabulary $VA$ (intersection of sentiment lexicon and super-parallel corpus) and vocabulary $V$ (arbitrary size and domain). In a first step, we compute the embedding space of $VA$ and $V$ using `fasttext` [@bojanowski2017] trained on the wikipedia. The embeddings for $VA$ are created using the super-parallel corpus. For $V$ any monolingual data can be used. In a second step, we quantify the domain shift between words in $VA$ and $V$ as follows:

1.  We compute a word similarity profile-based on the similarity distribution for the words in each embedding space by normalizing the cosine distance between that word and all the words in the embedding space

2.  Using the KL divergence between each word similarity profile in both spaces we compute a domain shift weight between word-pairs

Figure \[fig:domainShift\] illustrates the workings of our weighting method. Once we computed our shift weights, we incorporate them in our logistic regression model.

![image](divergence.pdf){width="110.00000%"}

Experiments and Evaluation {#experiments}
==========================

Experimental Setup {#setup}
------------------

Our goal is to evaluate the quality of *UniSent* against several manually created sentiment lexica in different domains to ensure its quality for the low resource languages. We do this in several steps.

#### Select Gold Standard Data

As the gold standard sentiment lexica, we chose manually created lexicon in Czech  [@veselovska2013czech], German [@waltinger2010germanpolarityclues], French [@abdaoui2014feel], Macedonian [@jovanoski2016impact], and Spanish [@perez2012learning]. These lexica contain general domain words (as opposed to Twitter or Bible). As gold standard for twitter domain we use emoticon dataset and perform emoticon sentiment prediction [@wiebe2005annotating; @hogenboom2013exploiting].

#### Create *UniSent*

We use the (manually created) English sentiment lexicon (WKWSCI) in [@khoo2018lexicon] as a resource to be projected over languages. For the projection step (Section \[projection\]) we use the massively parallel Bible corpus in [@Mayer2014]. We then propagate the projected sentiment polarities to all words in the Wikipedia corpus. We chose Wikipedia here because its domain is closest to the manually annotated sentiment lexica we use to evaluate *UniSent*. In the adaptation step, we compute the shift between the vocabularies in the Bible and Wikipedia corpora. To show that our adaptation method also works well on domains like Twitter, we propose a second evaluation in which we use *Adapted Sentiment Pivot* to predict the sentiment of emoticons in Twitter.

#### Create Test Sets

To create our test sets, we first split *UniSent* and our gold standard lexica as illustrated in Figure \[fig:datasplit\]. We then form our training and test sets as follows:

**(i) UniSent-Lexicon:** we use words in *UniSent* for the sentiment learning in the target domain; for this purpose, we use words $w \in A \cup C$.

**(ii) Baseline-Lexicon:** we use words in the gold standard lexicon for the sentiment learning in the target domain; for this purpose we use words $w \in B \cup C$.

**(iii) Evaluation-Lexicon:** we randomly exclude a set of words the **baseline-lexicon**$\cup B$. In selection of the sampling size we make sure that $UniSent-Lexicon$ and $Baseline-Lexicon$ would contain a comparable number of words.

![\[fig:datasplit\] Data split used in the experimental setup of UniSent evaluation: Set (C) is the intersection of the target embedding space words (Wikipedia/Emoticon) and the UniSent lexicon as well as the manually created lexicon. Set (A) is the intersection of the target embedding space words and the UniSent lexicon, excluding set (C). Set (B) is the intersection of the target embedding space words and the manually created lexicon, excluding set (C).](datascheme.pdf){width="35.00000%"}

Results {#evaluation}
-------

In Table \[tab:sentiment\_res\_1\] we compare the quality of *UniSent* with the Baseline-Lexicon as well as with the gold standard lexicon for general domain data. The results show that (i) *UniSent* clearly outperforms the baseline for all languages (ii) the quality of *UniSent* is close to manually annotated data (iii) the domain adaptation method brings small improvements for morphologically poor languages. The modest gains could be because our drift weighting method (Section \[adaptation\]) mainly models a sense shift between words which is not always equivalent to a polarity shift.\
In Table \[tab:sentiment\_res\_2\] we compare the quality of *UniSent* with the gold standard emoticon lexicon in the Twitter domain. The results show that (i) *UniSent* clearly outperforms the baseline and (ii) our domain adaptation technique brings small improvements for French and Spanish.

Conclusion
==========

Using our novel *Adapted Sentiment Pivot* method, we created *UniSent*, a sentiment lexicon covering over 1000 (including many low-resource) languages in several domains. The only necessary resources to create *UniSent* are a sentiment lexicon in any language and a massively parallel corpus that can be small and domain specific. Our evaluation showed that the quality of *UniSent* is closed to manually annotated resources.

[19]{} natexlab\#1[\#1]{}

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