Classifying Genuine and Fake Product Reviews with a Support Vector Machine

Support Vector Machine for Marketing and Consumer Behaviour

Field: Marketing and Consumer BehaviourLevel: Master's thesis (S2)Method: Support Vector Machine

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Using synthetic data for illustration, this analysis aims to separate genuine from fake reviews using simple review features. A Support Vector Machine finds the class boundary with the widest margin, and a radial kernel allows the boundary to curve. It suits theses on binary classification with numeric predictors whose relationship with the class is nonlinear.

Classifying Genuine and Fake Product Reviews with a Support Vector Machine: result sheet

Project info

Field of studyMarketing and Consumer Behaviour
LevelMaster's thesis (S2)
Method typeMachine Learning
Estimated build time± 5 working days
Toolse1071::tune.svm, e1071::svm, pROC::roc, pROC::auc, ggplot2
PriceUS$22 - US$147

Research goal

Using synthetic data for illustration, this analysis aims to separate genuine from fake reviews using simple review features.

Variables

Role	Variable	Unit
Main	Fake review	class
Supporting 1	Review length	count
Supporting 2	Star rating	stars

Example result

0.899Accuracy (test)
0.863Precision
0.936Recall
0.898F1 score
0.955AUC (test)
79Support vectors

A radial-kernel SVM tuned by 5-fold cross-validation (cost = 16.00, gamma = 0.062) classifies Fake review from Review length and Star rating with 89.9% test accuracy, F1 of 0.898 and AUC of 0.955. It uses 79 support vectors; discrimination is good and the nonlinear boundary is captured well.

Computed from illustrative synthetic data, not from a real study.

About Support Vector Machine

A Support Vector Machine finds the class boundary with the widest margin, and a radial kernel allows the boundary to curve. It suits theses on binary classification with numeric predictors whose relationship with the class is nonlinear.

Steps

1Prepare the data and code the class variable as a two-level factor
2Split the data into training and test sets (70:30, stratified by class)
3Tune cost and gamma of the radial kernel with 5-fold cross-validation (e1071::tune.svm)
4Fit the final model with the best parameters and probability = TRUE
5Compute accuracy, precision, recall, F1 and AUC on the test set
6Plot the decision surface, the CV accuracy heatmap and the ROC curve

Assumptions to check

Numeric predictors are scaled (svm does this by default)
Training data represent the class distribution of the target population
Cost and gamma are chosen by cross-validation, not from the test set

Reading and reporting the results

Report the best cost and gamma, then test accuracy, precision, recall, F1 and AUC. A large number of support vectors means many cases lie near the class boundary; the decision surface helps explain the shape of that boundary.

Example R code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

Show code

# Contoh kode R: Support Vector Machine
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(e1071)
  library(pROC)
})
set.seed(42)
# ---- Gaya grafik ----
pal <- c("#1f6feb", "#e8590c", "#2f9e44", "#ae3ec9", "#f59f00")
warna <- list(bg = "#f4f6fa", panel = "#ffffff", teks = "#1f2933", redup = "#5f6b78", grid = "#e3e8ee", bar = "#1d3557")
tata <- 1
theme_set(theme_minimal(base_size = 11) + theme(legend.position = "bottom"))

# ---- Data sintetis: kelas biner dengan batas keputusan nonlinear ----
n <- 200
positif <- function(x, m) if (m > 0) pmax(x, 0.05 * m) else x   # cegah nilai negatif yang tidak wajar
d <- data.frame(review_length = positif(rnorm(n, 50, 10), 50),
                star_rating = positif(rnorm(n, 30, 6), 30))
z1 <- as.numeric(scale(d$review_length))
z2 <- as.numeric(scale(d$star_rating))
arah <- sample(c(-1, 1), 2, replace = TRUE)
skor <- arah[1] * runif(1, 0.8, 1.3) * z1 + arah[2] * runif(1, 0.5, 1) * z2 +
  runif(1, 0.6, 1.1) * (z1^2 - 1) * sample(c(-1, 1), 1) + runif(1, 0.3, 0.7) * z1 * z2
skor <- skor - quantile(skor, runif(1, 0.4, 0.6))
d$fake_review <- rbinom(n, 1, plogis(runif(1, 2.5, 4) * skor))
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Pembagian data latih/uji (70:30, bertingkat per kelas) ----
d$kelas <- factor(d$fake_review, levels = c(0, 1), labels = c("No", "Yes"))
idx <- unlist(lapply(split(seq_len(n), d$kelas), function(i) sample(i, round(0.7 * length(i)))))
latih <- d[idx, ]
uji <- d[-idx, ]

# ---- Analisis: SVM kernel radial, tuning cost dan gamma dengan CV 5 fold ----
grid_cost <- 2^(-2:5)
grid_gamma <- 2^(-4:1)
tun <- tune.svm(kelas ~ review_length + star_rating, data = latih, kernel = "radial", cost = grid_cost, gamma = grid_gamma,
                tunecontrol = tune.control(sampling = "cross", cross = 5))
print(summary(tun))
c_best <- tun$best.parameters$cost
g_best <- tun$best.parameters$gamma
svm_fit <- svm(kelas ~ review_length + star_rating, data = latih, kernel = "radial", cost = c_best, gamma = g_best,
               probability = TRUE)
print(summary(svm_fit))

# ---- Evaluasi pada data uji ----
pr <- predict(svm_fit, uji, probability = TRUE)
prob <- attr(pr, "probabilities")[, "Yes"]
konf <- table(Actual = uji$kelas, Predicted = pr)
print(konf)
tp <- konf["Yes", "Yes"]; fp <- konf["No", "Yes"]; fn <- konf["Yes", "No"]
akurasi <- sum(diag(konf)) / sum(konf)
presisi <- if (tp + fp > 0) tp / (tp + fp) else 0
recall <- if (tp + fn > 0) tp / (tp + fn) else 0
f1 <- if (presisi + recall > 0) 2 * presisi * recall / (presisi + recall) else 0
roc_svm <- pROC::roc(uji$kelas, prob, levels = c("No", "Yes"), direction = "<", quiet = TRUE)
auc_svm <- as.numeric(pROC::auc(roc_svm))
n_sv <- nrow(svm_fit$SV)
cat(sprintf("Akurasi = %.3f, presisi = %.3f, recall = %.3f, F1 = %.3f, AUC = %.3f, SV = %d\n",
            akurasi, presisi, recall, f1, auc_svm, n_sv))


# ---- Grafik ----
gx <- seq(min(d$review_length), max(d$review_length), length.out = 120)
gy <- seq(min(d$star_rating), max(d$star_rating), length.out = 120)
bidang <- expand.grid(review_length = gx, star_rating = gy)
bidang$prob <- attr(predict(svm_fit, bidang, probability = TRUE), "probabilities")[, "Yes"]
latih$sv <- seq_len(nrow(latih)) %in% svm_fit$index
p1 <- ggplot() +
  geom_raster(data = bidang, aes(review_length, star_rating, fill = prob), alpha = 0.55, interpolate = TRUE) +
  geom_contour(data = bidang, aes(review_length, star_rating, z = prob), breaks = 0.5, colour = warna$teks, linewidth = 0.8) +
  geom_point(data = latih[latih$sv, ], aes(review_length, star_rating), shape = 21, size = 3.4, stroke = 0.7,
             colour = warna$teks, fill = NA) +
  geom_point(data = latih, aes(review_length, star_rating, colour = kelas), size = 1.5, alpha = 0.85) +
  scale_fill_gradient2(low = pal[1], mid = warna$panel, high = pal[2], midpoint = 0.5, guide = "none") +
  scale_colour_manual(values = c(No = pal[1], Yes = pal[2]), name = "fake_review") +
  coord_cartesian(expand = FALSE) +
  labs(title = "SVM decision surface (training data)", subtitle = "Shading = P(Yes), line = 0.5 boundary, rings = support vectors",
       x = "review_length", y = "star_rating")
pf <- tun$performances
pf$akurasi <- 1 - pf$error
pf$terbaik <- pf$cost == c_best & pf$gamma == g_best
p2 <- ggplot(pf, aes(factor(log2(gamma)), factor(log2(cost)), fill = akurasi)) +
  geom_tile(colour = warna$panel, linewidth = 0.6, alpha = 0.8) +
  geom_tile(data = pf[pf$terbaik, ], colour = warna$teks, fill = NA, linewidth = 1.1) +
  geom_text(aes(label = sprintf("%.2f", akurasi)), size = 2.4, colour = warna$teks) +
  scale_fill_gradient(low = warna$panel, high = pal[3], name = "CV acc.") +
  labs(title = "CV accuracy (cost x gamma)", x = "log2(gamma)", y = "log2(cost)") +
  theme(legend.position = "none", panel.grid = element_blank())
rd <- data.frame(fpr = 1 - roc_svm$specificities, tpr = roc_svm$sensitivities)
rd <- rd[order(rd$fpr, rd$tpr), ]
p3 <- ggplot(rd, aes(fpr, tpr)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = warna$redup) +
  geom_area(fill = pal[4], alpha = 0.15) +
  geom_line(colour = pal[4], linewidth = 1.1) +
  annotate("text", x = 0.62, y = 0.12, label = sprintf("AUC = %.3f", auc_svm), colour = warna$teks, size = 3.4) +
  labs(title = "ROC curve (test data)", x = "False positive rate", y = "True positive rate")
p_hasil <- if (tata == 1) (p1 | (p2 / p3)) + plot_layout(widths = c(1.35, 1)) else (p1 / (p2 | p3)) + plot_layout(heights = c(1.3, 1))

print(p_hasil)

The project file you can download

The original project file is available. It is not just the picture above: you get the working R project. It can be downloaded after payment, and we send it by WhatsApp or email.

ZIP: analisis.R + data_contoh.csv + BACA-DULU.txt

What is inside the ZIP

The complete analysis script (analisis.R), commented step by step, ready to run in RStudio.
A sample dataset (data_contoh.csv) so the script runs straight away.
A short guide (BACA-DULU.txt): which packages to install, how to run it and how to swap in your own data.

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

Package 1

Code and Explanation

US$22

excl. 11% VAT

Duration: 5 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for Support Vector Machine
Example data and step-by-step run instructions
Ready-to-use result figures and metrics table
Short explanation of the method, its assumptions and how to read the results

Choose Package 1

Package 2 · most chosen

Your Data Analysis + Interpretation

US$66

excl. 11% VAT

Duration: 9 working days

Output: Code + analysis + interpretation notes

Includes all of Package 1, plus

Adapting the code to your own research dataset (CSV or Excel)
Assumption checks, result validation and data cleaning
Tables and figures formatted for your manuscript
Interpretation notes for the methods and discussion sections, for you to write up in your own words

Choose Package 2

Package 3

Full Guidance

US$147

excl. 11% VAT

Duration: 14-17 working days

Output: Code + analysis + guidance until ready to present

Includes all of Packages 1 & 2, plus

Comparison against one alternative method with the reasoning for the choice
Two 60-minute online coaching sessions so you fully understand the code and results
Revisions following your supervisor feedback (up to 3 rounds)
Practice Q&A to prepare for your seminar and defence
Full documentation (README, R package list (sessionInfo)) so the results can be reproduced

Choose Package 3

Payment: 50% down payment before work starts, balance after the deliverables are handed over
Tax: Prices exclude 11% VAT
Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions

Do I get the real project file?

Yes. The original project file is a ZIP containing the complete analisis.R script, a data_contoh.csv sample dataset and a BACA-DULU.txt guide. It can be downloaded after payment, and we send it by WhatsApp or email. For your own data (Package 2 and 3) the script is adapted to your dataset.

Is this suitable for a Master's thesis (S2)?

Yes. Support Vector Machine is commonly used at this level in Marketing and Consumer Behaviour. We adapt the analysis to your research question, your own data and your supervisor's requirements.

Is the result sheet real data?

No. The sheet is computed from illustrative synthetic data so you can see the kind of output R Studio gives. For your order we run the same method on your own data.

Will I understand the code?

That is the point of the packages. Every script is commented, and Package 3 includes two coaching sessions plus practice questions so you can explain the method and the results at your seminar and defence.

How is the price decided?

Each project has three packages: Package 1 (code and explanation) US$22, Package 2 (your data analysis and interpretation) US$66 and Package 3 (full guidance) US$147, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

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The result sheet uses illustrative synthetic data. R is a programming language of the R Foundation project; RStudio and Posit are trademarks of Posit PBC.

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