DOI: 10.5281/zenodo.14178776
title: Bilingual Dataset of Multinational Brand Posts and Consumer Replies from Platform X (Turkish and English)

description: Overview

This dataset consists of posts and consumer replies from a curated selection of Fortune 500 companies that actively maintained Platform X (formerly Twitter) accounts in both English and Turkish. The sample includes 33 multinational brands spanning multiple industries, including fast-moving consumer goods (FMCG), fast food, technology, automotive, apparel, retail, finance, and logistics.

All Platform X messages posted by these brands, together with corresponding consumer responses, were collected over a five-year period (June 2016 – June 2021). The dataset captures both brand-initiated communication and public engagement, enabling large-scale analysis of emotional framing, and audience response patterns.

The full raw dataset contains:





English: 8,101,034 messages





2,708,874 brand posts




5,392,160 consumer replies






Turkish: 735,459 messages





245,422 brand posts




490,037 consumer replies





The matrices released here are derived representations of these messages, designed to support reproducible analysis while complying with platform redistribution policies.

What Is Released

This Zenodo deposit provides sparse frequency matrices representing:





Emoji usage per message




Seed-word usage per message



These representations enable interpretable analyses of:





emoji-based emotional signaling,




lexical emotion framing,




emoji–word co-usage patterns,




cross-language comparisons.



Tweet texts and tweet identifiers are not embedded in the matrices. Instead, deterministic alignment is ensured through explicit row identifiers.

Data Contents

The dataset is organized by language (tr, en). For each language, the following files are provided.

1. Frequency Matrices





{lang}_emoji_matrix_X.npzSparse emoji frequency matrix(rows = messages, columns = emoji features)




{lang}_seedword_matrix_X.npzSparse seed-word frequency matrix(rows = messages, columns = lexical features)



Matrices are stored in SciPy sparse CSC format for memory efficiency and scalable matrix operations.

2. Metadata Files





{lang}_emoji_matrix_meta.npz




{lang}_seedword_matrix_meta.npz



Each metadata file contains:





feature_names – ordered list of emoji or seed-word features corresponding to matrix columns




row_id – sequential integer identifiers (0…N−1) defining row alignment



The row_id ordering is identical across emoji and seed-word matrices within the same language.

3. Tweet Identifier Mapping





{lang}_tweet_ids.csv



Contains:





row_id – row identifier used in frequency matrices




tweet_id – Platform X message identifier



This file provides the only linkage between frequency matrices and original messages and allows tweet rehydration where permitted.

File Structure

.
├── tr_emoji_matrix_X.npz
├── tr_emoji_matrix_meta.npz
├── tr_seedword_matrix_X.npz
├── tr_seedword_matrix_meta.npz
├── tr_tweet_ids.csv
├── en_emoji_matrix_X.npz
├── en_emoji_matrix_meta.npz
├── en_seedword_matrix_X.npz
├── en_seedword_matrix_meta.npz
├── en_tweet_ids.csv


Seed Words and Emotion Lexicons

Seed words were selected from crowd-sourced emotion lexicons. For each language, the top-scored 90 words per emotion were retained to construct the seed-word frequency matrices.

English Seed Words

Derived from the NRC Emotion Lexicon:



Mohammad, S., & Turney, P. (2013).Crowdsourcing a Word-Emotion Association Lexicon.Computational Intelligence, 29(3), 436–465.


Turkish Seed Words

Derived from the Turkish Emotion Lexicon:



Toçoğlu, M. A., & Alpkoçak, A. (2019).Lexicon-based emotion analysis in Turkish.Turkish Journal of Electrical Engineering and Computer Sciences, 27(2), 1213–1227.https://doi.org/10.3906/elk-1807-41


These lexicons enable language-specific yet conceptually comparable emotion analysis.

Loading the Data (Python)

Requirements







 



numpy
scipy
pandas



Load Emoji Frequency Matrix

import numpy as np
import scipy.sparse as sp

X_emoji = sp.load_npz("tr_emoji_X.npz")
emoji_meta = np.load("tr_emoji_meta.npz", allow_pickle=True)

emoji_features = emoji_meta["feature_names"]
row_id = emoji_meta["row_id"]


Load Seed Word Frequency Matrix

X_seed = sp.load_npz("tr_seed_X.npz")
seed_meta = np.load("tr_seed_meta.npz", allow_pickle=True)

seed_features = seed_meta["feature_names"]


Load Tweet Identifier Mapping

import pandas as pd

df_tweets = pd.read_csv("tr_tweet_ids.csv", dtype={"tweet_id": str})

# Optional integrity check
assert (df_tweets["row_id"].to_numpy() == row_id).all()


Emoji–Word Co-Frequency via Dot Product

To quantify co-usage patterns between emojis and seed words, a dot product can be applied between the emoji and seed-word frequency matrices.

Let:





X_emoji ∈ ℝⁿˣᵉ (messages × emojis)




X_seed ∈ ℝⁿˣʷ (messages × words)



The co-frequency matrix is computed as:

C=Xemoji⊤XseedC=Xemoji⊤Xseed

Where:





C[i, j] represents the total number of messages in which emoji i and seed word j co-occur.



Compute Co-Frequency Matrix







 



C = X_emoji.T @ X_seed



Interpret Results





 




import pandas as pd

cofreq_df = pd.DataFrame(
    C.toarray(),
    index=emoji_features,
    columns=seed_features
)

# Example: seed words most frequently co-occurring with a given emoji
cofreq_df.loc["🙏"].sort_values(ascending=False).head(10)




This representation supports emotion framing analysis, emoji-anchored discourse interpretation, and interpretable CSR communication studies.

Code Availability

All preprocessing, analysis, and modeling code used in the associated paper is available at:

GitHub repositoryhttps://github.com/atanaltay/beyond_words_emoji_patterns.git

The repository includes scripts for:





data preprocessing and filtering,




matrix construction,




emoji–word co-frequency analysis,




downstream statistical and visualization analyses.



Reproducibility Notes





All matrices are raw frequency counts (not TF-IDF).




Feature ordering is explicitly stored.




row_id ensures deterministic alignment across files.




Tweet identifiers are distributed separately to comply with Platform X policies.




No implicit assumptions about row ordering are required.



Citation

If you use this dataset, please cite the associated paper and Zenodo record:



Citation details will be updated upon publication.


License and Usage

This dataset is released for academic research and non-commercial use.Users are responsible for complying with Platform X content redistribution and rehydration policies.